Myeloid Arginase 1 Insufficiency Exacerbates Amyloid-β Associated Neurodegenerative Pathways and Glial Signatures in a Mouse Model of Alzheimer’s Disease: A Targeted Transcriptome Analysis

Brain myeloid cells, include infiltrating macrophages and resident microglia, play an essential role in responding to and inducing neurodegenerative diseases, such as Alzheimer’s disease (AD). Genome-wide association studies (GWAS) implicate many AD casual and risk genes enriched in brain myeloid cells. Coordinated arginine metabolism through arginase 1 (Arg1) is critical for brain myeloid cells to perform biological functions, whereas dysregulated arginine metabolism disrupts them. Altered arginine metabolism is proposed as a new biomarker pathway for AD. We previously reported Arg1 deficiency in myeloid biased cells using lysozyme M (LysM) promoter-driven deletion worsened amyloidosis-related neuropathology and behavioral impairment. However, it remains unclear how Arg1 deficiency in these cells impacts the whole brain to promote amyloidosis. Herein, we aim to determine how Arg1 deficiency driven by LysM restriction during amyloidosis affects fundamental neurodegenerative pathways at the transcriptome level. By applying several bioinformatic tools and analyses, we found that amyloid-β (Aβ) stimulated transcriptomic signatures in autophagy-related pathways and myeloid cells’ inflammatory response. At the same time, myeloid Arg1 deficiency during amyloidosis promoted gene signatures of lipid metabolism, myelination, and migration of myeloid cells. Focusing on Aβ associated glial transcriptomic signatures, we found myeloid Arg1 deficiency up-regulated glial gene transcripts that positively correlated with Aβ plaque burden. We also observed that Aβ preferentially activated disease-associated microglial signatures to increase phagocytic response, whereas myeloid Arg1 deficiency selectively promoted homeostatic microglial signature that is non-phagocytic. These transcriptomic findings suggest a critical role for proper Arg1 function during normal and pathological challenges associated with amyloidosis. Furthermore, understanding pathways that govern Arg1 metabolism may provide new therapeutic opportunities to rebalance immune function and improve microglia/macrophage fitness.

Brain myeloid cells, include infiltrating macrophages and resident microglia, play an essential role in responding to and inducing neurodegenerative diseases, such as Alzheimer's disease (AD). Genome-wide association studies (GWAS) implicate many AD casual and risk genes enriched in brain myeloid cells. Coordinated arginine metabolism through arginase 1 (Arg1) is critical for brain myeloid cells to perform biological functions, whereas dysregulated arginine metabolism disrupts them. Altered arginine metabolism is proposed as a new biomarker pathway for AD. We previously reported Arg1 deficiency in myeloid biased cells using lysozyme M (LysM) promoter-driven deletion worsened amyloidosis-related neuropathology and behavioral impairment. However, it remains unclear how Arg1 deficiency in these cells impacts the whole brain to promote amyloidosis. Herein, we aim to determine how Arg1 deficiency driven by LysM restriction during amyloidosis affects fundamental neurodegenerative pathways at the transcriptome level. By applying several bioinformatic tools and analyses, we found that amyloid-b (Ab) stimulated transcriptomic signatures in autophagy-related pathways and myeloid cells' inflammatory response. At the same time, myeloid Arg1 deficiency during amyloidosis promoted gene signatures of lipid metabolism, myelination, and migration of myeloid cells. Focusing on Ab associated glial transcriptomic signatures, we found INTRODUCTION Dysregulation of arginine metabolism has been recognized to impact neuropathology and neuroinflammation in Alzheimer's disease (AD). Proper regulation of arginine metabolism through its different catabolizing enzymes (ARG1, ARG2, NOS1, NOS2, NOS3, ADC, AGAT1, ATE1) (1, 2) and protein sensors (GPRC6A, SLC38A9, CASTOR1, CASTOR2, TM4SF5) (3-6) remains critical for cellular responses to pathogenic stimuli, especially in myeloid cells. Recent metabolomics research in AD uncovered promising biomarker signatures associated with the deregulation of argininerelated pathways and polyamines in the blood and cerebral spinal fluid (CSF) (7)(8)(9)(10). The altered metabolism of arginine and arginase expression was confirmed in AD postmortem brains (11)(12)(13)(14)(15)(16). Human ARG1 has 43 mutations linked to ARG1 deficiency disorder, and a rare ARG2 variant was associated with a higher risk of AD (16,17). Increased arginine and altered Arg1 in the brain of animal models of aging and AD were also reported, signifying a pivotal role for proper arginine metabolism in neurodegeneration (15,(18)(19)(20)(21).
It has been reported that Arg1 is primarily expressed in braininfiltrating macrophages over microglia after central nervous system (CNS) injury and ischemia, and brain infiltrating macrophages relied on Arg1 expression to exhibit a reparative role to ameliorate damages (22)(23)(24)(25)(26). Most recently, as a marker, ARG1 distinguished human brain resident microglia from peripheral blood mononuclear cells (PBMCs) and cerebrospinal fluid mononuclear cells (CSF cells). The potential infiltrating CSF cells expressed the highest level of ARG1 among all three (27). The discrepancy of Arg1 expression between brain infiltrating macrophages and resident microglia complicates its role in myeloid cell subtypes and makes the entire brain myeloid cells (microglia/macrophages) a promising target for research and therapeutics (28)(29)(30)(31)(32). Albeit less studied in neurodegeneration, Arg1 activity is essential for controlling the inflammatory response of brain myeloid cells to an extracellular stimulus like amyloid-b (Ab). In animal models of AD, our group and several others have demonstrated that peripheral myeloid cells like monocytes could infiltrate into the brain as local tissue macrophages to clear Ab deposits together with activated brain resident microglia (33)(34)(35)(36). We showed that Arg1 overexpression in the CNS decreased the inflammatory response and ameliorated tau pathology (37). Another group showed that Arg1 positive microglia reduced Ab plaque burden under an IL-1b dependent inflammatory condition (38). Besides, myeloid-specific knockout of Arg1 in a retinal injury mouse model worsened neuronal loss and increased inflammatory responses (26).
Previously we sought to investigate the impact of reduced myeloid Arg1 in the APP Tg2576 mouse model of amyloidosis (39). We found that Arg1 insufficiency in lysozyme M (LysM) restricted cells produced more Ab deposition, increased activated microglia, and impaired behavioral performance. Moreover, amyloidosis induced machinery of the Ragulator-Rag complex responsible for phagocytosis. However, Arg1 deficiency blunted this response, suggesting a crucial role of arginine metabolism at the lysosome. While there is considerable evidence supporting that myeloid Arg1 deficiency during amyloidosis exacerbates ADtypical neuropathology and behavioral impairments, the underlying mechanisms have yet to be fully clarified. We performed bulk RNA transcriptome analysis to determine the transcriptional pathway changes following myeloid Arg1 haploinsufficiency during amyloidosis. We utilized the gene expression profiling with the nCounter ® mouse neuropathology panel (NanoString Technologies, Inc.) to analyze the top changed Ab associated neurodegenerative pathways and glial signatures. In the current study, our data suggest that insufficient myeloid Arg1 expression during amyloidosis activates transcriptomic pathways in myelination, lipid metabolism, and glial gene signatures that are primarily homeostatic and non-phagocytic. These data provide a more comprehensive transcriptional landscape of myeloid Arg1 insufficiency during amyloidosis, which could offer new therapeutic targets that improve myeloid function to mitigate amyloid deposition.

Bioinformatic Analyses
The nSolver Analysis The NanoString nCounter data analysis was performed using the nSolver ™ Analysis Software (v4.0, NanoString Technologies, Inc.) according to the user manuals (43,44). The nSolver was used for analyzing the mouse neuropathology panel by loading with an RLF file (NS_Mm_NeuroPath_v1.0). We performed the basic features of the nSolver analysis to generate the heat maps of differentially expressed gene transcripts (DEGs) with agglomerative clustering based on the normalized data. We also used it to export all normalized data. We also performed the advanced nSolver analysis based on the downloaded and installed online R package (nCounter_Advanced_Analysis_2.0.134) and the probe annotation file provided by NanoString (NS_Mm_NeuroPath_v1.0_ ProbeAnnotations). We modified this probe annotation file as the following description. First, we annotated two autophagy genes (Atg5, Becn1) to the autophagy pathway. Second, we referred to the so far most comprehensive GWAS publications in AD and annotated 15 gene transcripts into a new pathway named "AD Causal Risk Pathway" (Cntnap2, Cd33, Psen2, Mapt, Psmb9, App, Psen1, Apoe, Trem2, Adam10, Psmb8, Spi1, Sorl1, Clu, C4a) (45)(46)(47)(48)(49). All RNA samples passed system QC parameters on imaging (fields of view > 75), binding density (0.1-2.25), and positive control linearity (R 2 > 0.95). All RNA data passed the positive control limit of detection QC (0.5fM > 2*standard deviations above the arithmetic mean of the negative controls). The following basic threshold criteria were applied. A threshold count value of 10 was calculated as the gene transcript expression background (arithmetic mean of negative controls + 2*standard deviation). An observation frequency of 0.5 was applied for the background, indicating gene transcripts with counts lower than 10 were omitted if it happened to more than 50% of the samples. Therefore, 648 genes were above the background, and 122 genes were removed for falling below the background too frequently. A total of 6 house-keeping genes (Aars, Ccdc127, Cnot10, Csnk2a2, Lars, Mto1) were selected for data normalization by the geNorm algorithm (50). The p-values were adjusted for multiple testing by the false discovery rate (FDR) method of Benjamini and Hochberg correction (51). The matrix remodeling pathway was dropped from pathway scoring analysis due to less than five detected gene transcripts. In gene set analysis, a directed global significance score at ±1.3 was set as the cut-off value to reflect the top changed pathways further. Pathway scoring analysis and gene set analysis could independently assess a transcriptomic pathway's change due to different algorithms.

Selection of Genes for nSolver Cell-type Profiling Analysis
In cell-type profiling analysis, we performed advanced nSolver analysis to select cell-type-specific marker gene transcripts to characterize the major central nervous system cells (see Figure 4A). We identified astrocytes (52)(53)(54), endothelial cells (53), microglia/macrophages (53)(54)(55), neurotransmitter-secreting neurons (53), oligodendrocytes (53), and mature neurons (53,54). To clarify, the cell type scores can only be interpreted as relative cell abundance values rather than quantitative cell abundance in a group. Due to the counting and capturing efficiencies of individual cell-type-specific gene transcripts, one cell type score can be compared to the same cell type among different groups, but it cannot compare different cell types within the same group.

Selection of Focus Genes Indicative of Amyloid-b Associated Glial Transcriptomic Signatures for nSolver Analyses
Data from published transcriptomic studies using mouse models of amyloidosis were consulted to identify seven amyloid-b associated glial transcriptomic signatures. We cross-referenced these signature top prioritized genes with the NanoString mouse neuropathology panel ( Figure 5A). We selected 17 out of 57 gene transcripts that overlap with Plaque Induced Genes (PIGs;  purple WGCNA module genes in reference Table S3) and 30 out of 165 that overlap with Plaque Correlated Oligodendrocyte Genes (OLIGs; red WGCNA module genes in reference Table S3) (56). We selected 15 out of the top 117 that overlap with Disease-associated Microglia (DAM; sorted by p-values, -log10 (p-value) ≥ 21, up-regulated genes of "MG3/MG1" in reference Table S2 and Figure 6) and 9 out of total 36 that overlap with Homeostatic Microglia (HM; sorted by p-values, -log10 (pvalue) ≥ 7, down-regulated genes of "MG3/MG1" in reference Table S1 and Figure 6) (57). We selected 20 out of total 114 that overlap with Microglial Neurodegenerative Phenotype (MGnD; sorted by fold-change, log2 (FC) ≥ 0.01, up-regulated genes of "APP-PS1 10mo/WT 10mo" in reference Table S1) and 25 out of top 169 that overlap with Tolerogenic Microglia (M0; sorted by fold-change, log2 (FC) ≤ -0.43, down-regulated genes of "APP-PS1 10mo/WT 10mo" in reference Table S1) (58). We selected 15 out of the top 121 that overlap with Disease-associated Astrocytes (DAA; sorted by p-values, -log10 (p-value) ≥ 21, up-regulated genes of "Cluster 4/Cluster 1" in reference Table S2) (59).

IPA Analysis
Ingenuity ® Pathway Analysis (IPA ® , 2000-2020, QIAGEN) was applied for functional enrichment analysis and causal analysis using the differentially expressed gene transcripts (DEGs, p < 0.05), as previously described (60,61). IPA core analysis was performed to determine the top canonical pathways, predict upstream regulators, diseases/disorders, and biological functions based on two statistical measures (p-value and z-score). The p-value calculated the statistical significance of the overlap between the current dataset and the publicly available databases by a Fisher's exact test. The z-score implicates how likely the predicted state is activated/increased (z ≥ 2) or inhibited/decreased (z ≤ -2) based on a comparison model. The p-values less than 0.05 were enforced for all analyses in IPA. For analysis in canonical pathways, pathways were sorted based on p-values, and the top five most significant pathways were listed. For upstream analysis, upstream regulators were sorted by z-score to focus on predicted activation (z ≥ 2) or inhibition (z ≤ -2). A gene interaction network was plotted, including the predicted upstream regulators and associated target gene transcripts. IPA regulator effect analysis suggested potential mechanisms. Diseases or functions were sorted by z-score to show predicted increased (z ≥ 2) or decreased (z ≤ -2) risk. To reduce data redundancy, overlapped items (pathways/diseases/functions) were removed if they shared the same target gene transcripts and linked to unrelated peripheral organs/cancer/tumors.

STRING Analysis
STRING (Version 11.0), part of the ELIXIR Core Data Resources, is known for its predicted protein-protein interactions but also analyzes mRNA gene transcript in the current version (62). STRING was applied for network analysis and functional enrichment analysis using differentially expressed gene transcripts (DEGs, p < 0.05) with log2 fold change (FC) values. The Normal Gene Set Analysis was performed in STRING based on the mouse organism (Mus musculus, NCBI taxonomy Id: 10090). In our STRING network, nodes represent gene transcripts, and edges represent the expressed protein-protein association. The halo color of the nodes was based on the rank of DEG log2 FC. For setting STRING analysis, the network of edges was built on evidence by interaction lines (textmining, experiments, databases, co −expression) with medium confidence (0.4). Disconnected nodes were hidden in the network. STRING network with protein-protein interaction (PPI) enrichment p-value less than 0.05 indicates statistical significance, suggesting the expressed genes were biologically connected as a group. Functional enrichment analysis by Reactome Pathways was sorted based on an FDR q-value less than 0.01 and a minimum observed gene count of 5.

Experimental Design and Workflow of Gene Expression Profiling of Mouse Brains With APP Transgene and Arg1 Haploinsufficiency
To investigate the gene expression of arginase 1 in CNS cell types, we performed data-mining on 35 publicly available RNA sequencing (RNA-seq) studies using purified cell types from human and mouse brains (55,63). We found ARG1 gene transcript was particularly up-regulated in microglia/ macrophages (log2 FC = 3.228, adjusted p = 0.0006) but not in other CNS cell types in normal adult human brains (GSE73721) ( Figure 1A) (64). We also found Arg1 gene transcript was only up-regulated in microglia/macrophages (CD11b+, log2 FC = 2.777, adjusted p = 0.0067) in response to LPS stimulation in normal adult mouse brains, but not in astrocytes and neurons (GSE75246) ( Figure 1B) (65). Thus, we uncovered the arginase 1 transcript in these two RNA-seq datasets and found arginase 1 was mainly enriched and active in brain microglia/macrophages, aligning with our previous findings (39,(66)(67)(68). Therefore, we sought to repress Arg1 in myeloid cells to assess the impact of Arg1 deficiency in the mouse brain during the amyloidosis challenge.
The heat map of all normalized data displayed mouse clustering under two genotypes with 648 gene transcripts measured above the background expression threshold ( Figure S1A). All samples passed QC metrics and were automatically segregated by the two genotypes ( Figure S1A). Notably, samples were correctly clustered by the APP transgene genotype. Principal component analysis (PCA) calculated the first four principal components (PCs) of all data with PC1 (20%), PC2 (11%), PC3 (10%), and PC4 (9%) plotting to each other against the four groups ( Figure S1B). Accounting for the highest percentage of genotype variance, the PC1 identified modest but clear sample separation due to the APP transgene genotype, but to a lesser extent for the Arg1 haploinsufficiency genotype ( Figure S1B). It suggests that the overexpression of human APP transgene dominates the overall differential expression of genes, whereas the myeloid Arg1 haploinsufficiency becomes a secondary influencing factor.

Pathway Scoring Analysis of Fundamental Neurodegeneration Pathways in Mouse Brains With APP Transgene and Arg1 Haploinsufficiency
All genes were annotated in 25 gene signature pathways of neurodegeneration (two disease pathways and 23 functional pathways) that were categorized into seven fundamental themes (one disease theme and six functional themes) ( Figure 2A). To analyze each gene signature pathway, pathway scoring analysis (PSA) was performed to create a score for each group using the first principal component dataset of the pathway's gene set (44,88). The sum of all four groups equilibrated to zero value in each pathway, while their scores indicated up or down-regulation relative to each other. Collectively, pathway scores were visualized in a heat map to represent all pathways' clustering across samples under two genotypes. We observed clustering of the APP transgene genotype between APP mice (APP +/-/LysMcre Tg/+ ) and nTg mice (nTg/LysMcre Tg/+ ), and clear sub-clustering of Arg1 insufficient mice (Arg1 fl/+ /LysMcre Tg/+ ) and Arg1 sufficient mice (Arg1 +/+ /LysMcre Tg/+ ) except for one sample ( Figure 2B).
Then we statistically analyzed the 25 gene signature pathway scores and described them here. Compared to nTg mice, we found that APP mice up-regulated eight pathways and downregulated one pathway due to the main genotype effect in APP transgene. The eight up-regulated pathways were AD causal risk pathway (p < 0.0001, Figure 2C), disease association (p = 0.002, Figure 2D), neuronal cytoskeleton (p < 0.0001, Figure 2E), autophagy (p < 0.0001, Figure 2F), activated microglia (p < 0.0001, Figure 2H), cytokines (p = 0.002, Figure 2I), angiogenesis (p = 0.010, Figure 2J), and myelination (p < 0.0001, Figure 2L). The one down-regulated pathway was transmitter synthesis and storage (p = 0.021; Figure 2M). On the other side, we did not find any main genotype effect in Arg1 haploinsufficiency except for an increasing trend in apoptosis (p = 0.083, Figure 2K). However, we observed increased myelination (p = 0.023, Figure 2L) and an increasing trend in lipid metabolism (p = 0.054, Figure 2G) with the pairwise comparison of APP/Arg1 insufficient mice to APP/Arg1 sufficient mice. We also found an increased trend for two genotypes' interaction in myelination (p = 0.058, Figure 2L) and lipid metabolism (p = 0.087, Figure 2G). In addition, the heat map of the correlation matrix of all gene signature pathways clearly separated pathways of up-regulation and downregulation into the opposite direction ( Figure S1C). To summarize, the APP transgene exerted stronger effects on gene expression than did Arg1 haploinsufficiency, mostly by increasing transcriptomic pathways associated with the risk of neurodegeneration diseases (AD causal risk/disease association) and neuroinflammation (activated microglia/cytokines).

Gene Set Analysis for Measuring Differential Expression of Neurodegeneration Pathways in Mouse Brains With APP Transgene and Arg1 Haploinsufficiency
To assess changes of signature pathways in APP transgene and Arg1 haploinsufficiency genotypes, we performed gene set analysis (GSA) to measure the cumulative evidence for the differential expression of a pathway's gene set. GSA calculates a directed global significance score (GSS) for each pathway based on the square root of the mean (signed) squared t-statistic of the gene set's genes (44). Positive or negative GSS values indicate a pathway is overall up or down-regulated, respectively. We also adopted a GSS threshold value of ± 1.3 to indicate significance. The GSA heat map clearly segregated pathways of up-regulation and down-regulation ( Figure 3A).

Cell-type Profiling Analysis of Central Nervous System Reveals APP Transgene Activates Microglia/Macrophages and Myeloid Arg1 Deficiency During Amyloidosis Promotes Oligodendrocytes
We performed a cell-type profiling analysis (CPA) on CNS cells using their cell-type-specific gene transcripts to explore which cell types changed across four groups due to the two covariates of genotype. We first applied a QC p-value to investigate the validity of measuring each cell type. Statistically significant cell types (QC p ≤ 0.05) indicate the selected marker gene transcripts exhibit a significant cell-type-specific correlation than randomly selected genes with a similar size. This stringent algorithm requires selecting cell-type-specific genes that are consistently expressed above background while keeping a correlation expression slope close to 1 (44,89). Therefore, we confidently characterized cell types of astrocytes (QC p < 0.0001), endothelial cells (QC p < 0.0001), microglia/macrophages (QC p < 0.0001), neurotransmitter-secreting neurons (QC p = 0.010), oligodendrocytes (QC p = 0.040), but not the mature neurons (QC p = 0.060) ( Figure 4A). The CPA heat map presented the clustering of cell types (QC p ≤ 0.05) corresponding to genotypes of APP transgene and Arg1 haploinsufficiency ( Figure 4B). The CPA line plot showed the relative cell-type transcriptomic changes in selected marker gene transcripts' abundance across groups ( Figure 4C). Furthermore, the average log2 counts of the selected marker gene transcripts were calculated as the cell type scores. Comparing APP/Arg1 sufficient mice with nTg/Arg1 sufficient mice, we observed increased cell type score only in microglia/macrophages (Fcrls, Itgam, Trem2, Irf8, CD68, Hexb, Cx3cr1; p = 0.001, Figure  4D), but not other CNS cell types including oligodendrocytes (Mog, Pllp, Plxnb3, Gsn, Fa2h, Mag; p = 0.351, Figure 4E). Surprisingly, APP/Arg1 insufficient mice showed increased cell type score over the nTg/Arg1 insufficient mice in both microglia/macrophages (p = 0.001, Figure 4D) and oligodendrocytes (p = 0.038, Figure 4E). Therefore, both microglia/macrophages (p < 0.0001, Figure 4D) and oligodendrocytes (p = 0.04, Figure 4E) suggested main effects in APP transgene genotype. Collectively, the CPA data strongly suggest that APP transgene activates microglia/macrophages, and myeloid Arg1 deficiency during amyloidosis promotes oligodendrocytes, by which CNS cell types presumably drive the regulation of neurodegeneration.
We have shown that brain myeloid cells (microglia/ macrophages) express the highest arginase 1 among CNS cell types ( Figures 1A, B), and they were also activated in APP mice ( Figure 4D). This would suggest that Arg1 deficiency in myeloid cells may affect their normal immune response and reparative role upon various stimuli. It is unknown if myeloid Arg1 deficiency impacts the glial signatures during the challenge of the Ab stimulus. To address this question, we systematically matched our data with those above seven major published Ab associated glial gene signatures solely characterized in mouse models of amyloidosis ( Figure 5A).
We first performed PSA and displayed it in a heat map, indicating the significant role of APP transgene in segregating the APP mice from the nTg mice with extensive gene signature overexpression ( Figure 5B). We also presented a line plot showing that the APP mice had an increased signature score than the nTg mice, whereas the APP/Arg1 insufficient mice kept the highest score in all glial signatures than any other groups ( Figure 5C). Collectively, we collated the seven signatures and merged them as amyloid-b associated glial transcriptomic signatures. We observed an increased signature score in the main genotype effect of APP transgene (p < 0.0001) and its pairwise comparison between APP/ Arg1 sufficient mice and nTg/Arg1 sufficient mice (p = 0.003). Most importantly, the APP/Arg1 insufficient mice had an increased score than the APP/Arg1 sufficient mice (p = 0.049). ( Figure 5D).
Combining PSA with GSA, we found gene signatures of PIGs  Figure 5F). Notably, myeloid Arg1 deficiency in APP mice further activated the PIGs as its top changed signature while promoting HM as the second ( Figure  5F). To summarize, these data suggest that myeloid Arg1 deficiency enhances Ab associated glial transcriptomic signatures leaning towards homeostatic and non-phagocytic directionality.
Human APP Transgene Elevates Gene Signatures Associated With Autophagy, Activated Microglia and Inflammatory

Response of Myeloid Cells in Mouse Brain
To measure the effect of human APP (KM670/671NL, Swedish) overexpression in Arg1 sufficient mice, we performed differential gene expression analysis by comparing APP/Arg1 sufficient mice to nTg/Arg1 sufficient mice and thus identified 47 differentially expressed gene transcripts (DEGs, p < 0.05) ( Table S1). A clustering heat map segregated the two groups based on these DEGs ( Figure 6A). The top DEGs with high statistical significance and fold change variance could be visualized in the volcano plot ( Figure 6B). Furthermore, we created PCA biplots using the top 15 DEGs based on log2 fold change. The upregulated DEGs (Irf8, C4a, Gfap, Trem2, Mta2, Gba, Cd68, Stab1, Osmr, Hmox1, Cd33, Fcrls) and down-regulated DEGs (Ncl, Lpar1, Ache) successfully separated the two groups by a leading PC1 (54.7%) and a PC2 (14.4%) ( Figure 6C). The APP/Arg1 sufficient mice had increased transcriptomic pathway scores than nTg/Arg1 sufficient mice in autophagy (p = 0.001, Figure 2F), activated microglia (p = 0.010, Figure  2H), AD causal risk pathway (p = 0.002, Figure 2C), neuronal cytoskeleton (p = 0.006, Figure 2E), disease association (p = 0.041, Figure 2D), and myelination (p = 0.003, Figure 2L) ( Figure 6D). The DEGs of these pathways were also listed ( Figure 6E). The GSA showed that the top 3 up-regulated pathways that met the cut-off GSS criterion were autophagy (GSS = 1.900), activated microglia (GSS = 1.672), and AD causal risk pathway (GSS = 1.665) ( Figure 6D). We observed 13 out of the top 15 DEGs contributed to the top changed pathways identified by PSA and GSA ( Figure 6E). Importantly, the AD causal risk pathway has 60% microglia related genes in its gene set (Cd33, Psen2, Psmb9, Apoe, Trem2, Psmb8, Spi1, Clu, C4a) and its up-regulated DEGs (C4a, Trem2, Cd33) overlapped with activated microglia pathway ( Figure 6E). Notably, CPA showed that the APP/Arg1 sufficient mice activated microglia/macrophages cell-type-specific gene transcripts than nTg/Arg1 sufficient mice ( Figure 4D). These transcriptomics data suggest that Ab plaque activated brain myeloid cells with increased autophagy. Next, we performed Ingenuity Pathway Analysis (IPA) based on the DEGs mentioned above. First, IPA identified the top five canonical pathways (based on p-values) were neuroinflammation signaling pathway, acute phase response signaling, dendritic cell maturation, role of Jak family kinases in Il-6-type cytokine signaling, and endothelin-1 signaling ( Figure 7A). Importantly, the neuroinflammation signaling pathway was the top activated pathway with the highest statistical significance (p = 9.33E-09) and the most target gene transcripts (Bcl2, Calb2, Grin1, Hmox1, Il6, Mapk8, Rela, Stat1, Trem2) ( Figure 7A). Secondly, IPA predicted activated upstream regulators (Il6, App; z ≥ 2.0) and inhibited upstream regulators (Notch1, Tsc2; z ≤ -2.0) ( Figure 7B). By displaying the predicted upstream regulators in a hierarchical gene interaction network, we observed that App was the top-notch upstream regulator that causes the entire network with the highest significance (p = 5.90E-09, z = 2.037) and the most target gene transcripts (Ache, Bcl2, C4a/C4b, Cd68, Gfap, Gnao1, Grin1, Hmox1, Il6, Mbp, Nos1, Rela, Sirt1, Stx1b) ( Figures 7B, D). Moreover, IPA built two networks of regulatory effects based on the four predicted upstream regulators. One network was composed of Il6, Notch1, and Tsc2, pointing to one biological function in macrophages' immune response with four target gene transcripts (Grn, Hmox1, Irf8, Rela). Another network was by App alone linked to one disease in motor dysfunction with five target gene transcripts (Bcl2, Gfap, Grin1, Il6, Sirt1). Next, we used IPA to study the top diseases/disorders and biological functions. It predicted neurological diseases with increased motor dysfunction and movement disorders (z ≥ 2.0) ( Figure 7C). It also predicted that the inflammatory response was the top (based on z-score) increased biological function, including the increased immune response of cells, accumulation of phagocytes, immune response of macrophages, and immune response of antigen presenting cells (z ≥ 2.0) ( Figure 7C). It also predicted other functions in lipid metabolism (increased synthesis of glycolipid and synthesis of lipid, z ≥ 2.0), cell death and survival (increased cell death of epithelial cells, z ≥ 2.0; decreased cell viability of epithelial cell lines, z ≤ -2.0), as well as nervous system development (decreased long-term potentiation, z ≤ -2.0) ( Figure 7C). Lastly, we performed STRING analysis and plotted the connected DEGs to generate a gene interaction network (PPI p-value < 1.0E-16), highlighted by significant pathways identified by Reactome Pathways. These were innate immune system (q = 0.0028) and immune system (q = 0.0063) (Figures 7E, F). Overall, these data strongly suggest overexpression of human APP (KM670/671NL, Swedish) in mouse brain activates gene signatures of innate immune response associated with brain myeloid cells.
We then applied IPA on the DEGs and observed the top five canonical pathways (based on p-values) consisting of the neuroinflammation signaling pathway, glucocorticoid receptor signaling, synaptogenesis signaling pathway, Ephrin receptor signaling, and PEDF signaling ( Figure 9A). Notably, the most significant neuroinflammation signaling pathway had the highest activation z-score ((p = 1.66E-07, z = 1.633) and the most target gene transcripts (Akt2, Bcl2, Creb1, Fas, Gabrb3, Grin2d, Tgfbr2) ( Figure 9A). IPA predicted one activated upstream regulator (Il5, z ≥ 2.0, biased) and one inhibited upstream regulator (Pten, z ≤ -2.0) with an interactive gene regulation network ( Figures 9B, D). Furthermore, IPA further built one upstream regulatory network based on Il5 with five target gene transcripts (Bcl2, Cd9, Fas, Il4r, Slc2a1) and was associated with three biological functions (formation of lymphoid tissue, migration of cells, vasculogenesis). Furthermore, although no disease was predicted by IPA (z ≥ 2.0 or z ≤ -2.0), we found that neurological diseases were increased with a trend in neurodegeneration of sensory neurons (z = 1.980) and injury of nervous system (z = 1.678) ( Figure 9C). IPA successfully predicted that the top changed function was a cellular movement in biological functions, with increased migration of cells, cell movement, and cell movement of myeloid cells (z ≥ 2.0) ( Figure 9C). Other predicted functions included cardiovascular system development (increased angiogenesis and vasculogenesis, z ≥ 2.0), lymphoid tissue structure and development (increased formation of lymphoid tissue and lymphopoiesis, z ≥ 2.0),  Table S1. An absolute z-score value at 2.0 was set as the cut-off criterion to predict the activated/increased (z-score > 0) or inhibited/decreased (z-score < 0) states, and were highlighted in red or green. (D) IPA network displays the predicted upstream regulators in a hierarchical order. Upstream regulators predicted as activation or inhibition are colored in orange or blue. DEGs are colored in red or green, indicating increased or decreased expression. DEGs involved in Alzheimer's disease are also highlighted with a purple border. Direct or indirect gene interactions are in solid or dash lines with orange or blue colors indicating activation or inhibition. The intensity of color shading represents either measured fold-change magnitude or predicted activation/inhibition magnitude. (E) STRING network of the connected DEGs is presented. Each node represents a DEG with a halo color of red to blue, indicating high to low values of log2 fold-change. Nodes in different colors are annotated based on the top Reactome Pathways. Each edge line represents an interaction between two DEGs based on parameters of databases (cyan), experiments (purple), textmining (yellow), and co-expression (black). (F) A table lists the top Reactome Pathways from STRING functional enrichment analysis. The false discovery rate (FDR) q-value is set at ≤ 0.01. The observed gene count is set at ≥ 5. DEGs highlighted in red or green indicate up or down expression. n=3 samples per group representing 6 mice.  Table S2. An absolute z-score value at 2.0 was set as the cut-off criterion to predict the activated/increased (z-score > 0) or inhibited/decreased (z-score < 0) states and were highlighted in red or green. (D) IPA network displays the predicted upstream regulators in a hierarchical order. Upstream regulators predicted as activation or inhibition are colored in orange or blue. DEGs are colored in red or green, indicating increased or decreased expression. DEGs involved in Alzheimer's disease are also highlighted with a purple border. Direct or indirect gene interactions are in solid or dash lines with orange or blue colors indicating activation or inhibition. The intensity of color shading represents either measured fold-change magnitude or predicted activation/inhibition magnitude. (E) STRING network of the connected DEGs is presented. Each node represents a DEG with a halo color of red to blue, indicating high to low values of log2 fold-change. Nodes in different colors are annotated based on the top Reactome Pathways. Each edge line represents an interaction between two DEG transcripts based on parameters of databases (cyan), experiments (purple), textmining (yellow), and co-expression (black). (F) A table lists the top Reactome Pathways from STRING functional enrichment analysis. The false discovery rate (FDR) q-value is set at ≤ 0.01. The observed gene count is set at ≥ 5. Gene transcripts highlighted in red or green indicate up or down expression. n=3 samples per group representing 6 mice. cellular growth and proliferation (increased cytostasis, z ≥ 2.0), gene expression (increased transcription of RNA, z ≥ 2.0), cell death and survival (increased cell death of sensory neurons, z ≥ 2.0), and organismal survival (decreased organismal death, z ≤ -2.0) ( Figure 9C). Finally, the STRING network of interacted DEGs was established (PPI p-value = 9.28E-05) ( Figure 9E). The STRING functional enrichment analysis identified significant pathways via Reactome Pathways in transmission across chemical synapses (q = 0.0022), innate immune system (q = 0.0074), immune system (q = 0.0074), and metabolism of lipids (q = 0.0074), all of which were annotated in the network (Figures 9E, F). Collectively, these transcriptomic findings suggest myeloid Arg1 deficiency activates gene signatures of lipid metabolism and myelination and promotes myeloid cell migration in the mouse brain of amyloidosis.

DISCUSSION
Together with our previous findings reporting that myeloid Arg1 insufficiency precipitates Ab deposition (39), the current transcriptomic analysis shows that myeloid Arg1 insufficiency activates Ab plaque-associated glial gene signatures to exacerbate neurodegeneration. First, we demonstrated that the APP transgene up-regulated pathways most related to autophagy, activated microglia, and AD causal risk, while Arg1 haploinsufficiency up-regulated pathways of lipid metabolism and myelination. Second, we demonstrated that APP transgene mostly activated microglia/macrophages and myeloid Arg1 deficiency during amyloidosis promoted oligodendrocytes by analyzing cell-type-specific gene expression. Next, we provided strong evidence from analyzing key Ab plaque-associated glial transcriptomic signatures to support the notion that APP transgene activated these signatures mostly by inducing disease-associated microglial genes, whereas myeloid Arg1 haploinsufficiency increased them further by largely eliciting homeostatic microglial genes. Collectively, this is the first report to suggest that Arg1 deficient brain myeloid cells align transcriptome signatures that may phagocytose less Ab plaques, thus aggravating the accumulation of Ab plaques and possibly neurodegeneration.
Microglia perform various functions, maintaining CNS homeostasis during normal aging and front-line responders and inducers for neurodegenerative diseases like AD (90,93,94). Earlier work from Elly Hol and others identified several Ab associated microglial pro-inflammatory transcriptional profiles in different amyloidosis mouse models based on extensive microarray data (95)(96)(97)(98). Eventually, all of these microglial profiles pointed to a common chronic primed microglia transcriptional signature established by gene co-expression meta-analysis (77), from which the results were built into the NanoString nCounter ® mouse neuropathology panel. Due to recent developments in scRNA-seq and targeted NanoString nCounter techniques, researchers have confirmed these earlier findings by further deciphering the various transcriptional subtypes and distinct stages of microglia that were interacting with Ab plaques (56-59, 99, 100) (Figure 5A), thus converging age, sex, and AD risk genes as the major risk factors for AD (101,102). Importantly, critical microglial activation genes identified in amyloidosis mice were also recently validated in human AD brains (103). Albeit with the conflicting data interpretation, microglia may show either beneficial or detrimental effects depending on aging and disease progression by inducing disease-associated microglial signatures or restoring homeostatic microglial signatures (104,105). Microglial fitness required to dynamically switch between these states is critical for disease pathology. It was shown that the homeostatic microglial signature (HM/M0) is mostly nonphagocytic and presumably becomes suppressed to initiate the disease-associated microglial signature (DAM/MGnD) that is more phagocytic (57,58). Therefore, microglia locked into a homeostatic state may be just as detrimental as the diseaseinduced state.
Our study focused on seven critical glial transcriptomic signatures involved in amyloidosis from the literature covering both homeostatic and disease-associated microglia. First, our results showed the APP transgene activated Ab plaque induced genes (PIGs) as the top changed signature ( Figure 5E) and myeloid Arg1 deficiency during amyloidosis up-regulated it further ( Figure 5F). The findings were in line with our previous report that myeloid Arg1 insufficiency promoted amyloidosis (39), confirming that the magnitude of PIGs positively correlated to the load of Ab deposition (56). Next, further investigation of individual glial signatures revealed that myeloid Arg1 deficiency during amyloidosis preferentially activated homeostatic microglia (HM) gene signature. Therefore, we predict that the transcriptomic change in migration of myeloid cells caused by myeloid Arg1 deficiency may be a compensatory response to the increased Ab burden due to the non-phagocytic homeostatic microglial signature.
Recent studies found that the pre-classical M2 marker gene Arg1 played an essential role in activities of phagocytosis and efferocytosis by myeloid cells. One study showed that Arg1, orchestrated by STAT6/Arg1 signaling axis, was responsible for efferocytosis of microglia/macrophages to remove dead/dying neurons (106). Another group also showed that the Arg1 was involved in the continual efferocytosis process of engulfing and degrading apoptotic cells to provide nutrients (107). One study reported that Arg1 was the second most up-regulated gene transcript for inducing phagocytic microglial signature (MG-dNF) relative to the non-phagocytic microglial signature (MG-nF) after injection of apoptotic neurons in mouse brain (58). Therefore, the simplicity of viewing microglial activation phenotypes as M1-like (pro-inflammatory) and M2-like (antiinflammatory) was further challenged and has been replaced by recent scRNA-seq based discoveries of molecular subtyping microglial phenotypes (90,104,108,109). These findings are consistent with our previous in vitro work demonstrating that repressing Arg1 in microglial cells impaired phagocytosis (39). Thus, Arg1 is a key microglial functional marker for facilitating phagocytosis and efferocytosis and, when suppressed, could promote non-phagocytic and homeostatic microglial signatures.
Recent progress in studying AD pathophysiology using single-cell and spatial transcriptomics in AD patients and APP knock-in mouse models discovered myelination-related pathways were disturbed mostly in oligodendrocytes but also in other principal CNS cells (56,110). Our data linked myeloid Arg1 deficiency with the myelination gene set during the amyloid challenge. First, pathway analyses revealed that myelination gene signature was up-regulated due to myeloid Arg1 haploinsufficiency in APP mice (Figures 2L and 8D). Second, imputing gene expression changes to CNS cells showed oligodendrocytes as one of the two significantly changed cell types. Oligodendrocyte-specific gene transcripts were only increased when the APP transgene was expressed in the myeloid Arg1 haploinsufficient background mice ( Figure 4E). Third, pathway analysis of amyloid-b associated glial transcriptomic signatures showed that the signature of Ab plaque correlated oligodendrocyte genes (OLIGs) was upregulated when suppressing myeloid Arg1 in APP mice ( Figure 5F). The OLIGs signature was previously reported to correlate positively with the Ab burden in specific brain regions (entorhinal cortex and hippocampus) (56), thus suggesting that increased myelination could be a secondary effect from myeloid Arg1 deficient mice since we previously reported these mice presented more Ab deposition (39). Therefore, these data might suggest a potential role of Arg1 in the crosstalk between microglia and oligodendrocytes. During conditions of neurodegenerative diseases, oligodendrocytes continue an active demyelination/ remyelination process, in which microglia and reactive astrocytes both play a role (111). Our study remains unclear if/ how Arg1 deficient microglia regulate myelination during amyloidosis based on transcriptomic evidence. However, previous research demonstrated that increased Arg1 served as a dominant switch in microglia for initiating the remyelination process (112) and that microglia could acquire a proregenerative state with increased Arg1 (113,114). Furthermore, we also found that the oligodendrocyte-specific gene transcript marker Mog, which was elevated in Arg1 deficient APP mice in this study ( Figure 8E), was previously identified as a critical CNS-specific autoantigen responsible for demyelination in multiple sclerosis (115,116). Therefore, our data suggest that reduced Arg1 in myeloid cells promotes transcript signatures associated with demyelination or delayed remyelination during the amyloid challenge.
Furthermore, our pathway analyses showed that myeloid Arg1 haploinsufficiency during amyloidosis also up-regulated the lipid metabolism gene set ( Figures 2G and 8D). Lipid pathology has been validated as a shared feature between neurodegenerative mouse models and human AD (63,117,118). A recent study in aging and AD identified a substantial diseased microglial population termed "lipid-droplet-accumulating microglia (LDAM)", which were defective in phagocytosis (119). The gene set analysis showed the glial signature LDAM was up-regulated in myeloid Arg1 deficient APP mice without meeting significance (data not shown). While it is feasible that activated brain lipid metabolism is partly due to LDAM, this warrants further investigation. Interestingly, acetylcholinesterase (Ache), a therapeutic target for AD, was one of the top three gene transcripts up-regulated when reducing myeloid Arg1 in APP mice ( Figures 8C, E). It is known that Ab peptides increased Ache (120), and conversely, Ache promoted Ab production (121,122), which aligned with our current finding of increased Ache expression with elevated Ab plaque-induced genes. Increased Ache could also decrease cholinergic transmission and contributing to cognitive impairment (123). We identified the top changed network by Reactome Pathway was transmission across chemical synapses with increased Ache, Grin2d, and Raf1, and decreased Creb1 and Gabrb3, all of which could contribute to cognitive dysfunction ( Figure 9F). These findings were in line with our previous observation that myeloid Arg1 deficiency during amyloidosis hastened mouse behavioral impairments (39).
Therefore, by analyzing gene expression profiling in fundamental neurodegeneration pathways, we provided novel transcriptomic mechanisms to corroborate the previous observation that myeloid Arg1 deficiency exacerbated Ab deposition by promoting gene sets essential for myelination, lipid metabolism, and activating Ab associated glial genes biased for homeostatic/non-phagocytic microglia. By laying a foundation for the role of Arg1 in phagocytic myeloid cells during amyloidosis, we provided a new therapeutic target for manipulating arginine metabolism through arginase 1 to benefit human AD. Considering the beneficial role of overexpressing Arg1 in the tau transgenic mouse model (37), a future study on overexpressing Arg1 in a mouse model of amyloidosis should be investigated. Conversely, another study using the CVN-AD mouse model (Nos2 null) showed that sustained elevated extracellular Arg1 level stimulated amyloidosis and promoted hippocampal neuronal death (21). These discrepant studies implicate that temporal and spatial Arg1 activity in different CNS cell types, animal models, aging stages, and disease progression remain critical questions for future studies.
Critically, we need to mention that transcriptomic analyses cannot prove biological cell function or phenotype changes because there are many gene regulation levels besides transcription. Future studies are needed to confirm that the gene expression changes reported herein result in changes in protein levels or and that these changes modify cellular phenotype. It is important to remember that the pathway analyses identify coordinated regulation of multiple genes associated with given cell functions. Consequently, spurious errors in expression of a single gene transcript cannot explain the results we observed linking specific pathways to amyloid or Arg1 insufficiency.
Although the LysMcre mice have been a useful tool to mainly target myeloid cells for many years (71,124), the specificity of cell types that LysMcre targeted to suppress Arg1 expression has been questioned recently (125). Two characterization studies on LysMcre specificity in the mouse brain were thus reported. One group showed that the LysM promoter was almost exclusively active in neurons rather than microglia within certain brain regions (neuronal layer of the forebrain motor cortex and granule cell layer of the cerebellum), but on average, it was active in less than 30% of both neurons and microglia across the whole brain (126). Another recent report found that the LysMcre promoter was active in 40% of macrophage/microglia and only 8% of neurons in adult mouse retina (127). Although the LysMcre promoter was still validated to express greatest in myeloid cells, both studies provided evidence to show the existence of recombination in neurons, albeit to different levels.
Although the functional role of the antibacterial enzyme LysM in neurons is still unclear, recent scRNA-seq studies in microglia showed that Lyz2 encoding LysM was one of the commonly induced microglial genes during neurodegenerative diseases (104). Lyz2 was up-regulated in disease-associated microglial signatures such as PIGs (56), DAM (57), and MGnD (58), indicating that the brain myeloid cells most likely up-regulate Lyz2 as a compensatory response to amyloid stimulation. These recent findings using scRNA-seq in amyloid-depositing mouse models confirmed one previous study in human AD, which showed that lysozyme protein was increased in the CSF of AD patients, co-localized with Ab plaque in postmortem AD brains, and directly interacted with Ab in vitro (128). The increase of secreted lysozyme in CSF was thus attributed to the mononuclear monocytes/macrophages. Therefore, the current evidence suggests a protective role of up-regulating lysozyme in responding to Ab, a process that mainly occurs in brain myeloid cells rather than other CNS cells including neurons. These findings indicate that LysMcre dependent Arg1 haploinsufficiency has a functional consequence in brain myeloid cells. However, it is possible that the one allele deletion of Lyz2 due to the insertion of Cre-recombinase also caused unknown effects. Since all the mice shared the same Lyz2 haploinsufficient background, the unintended expression should be present in all mice and should minimize putative effects on differentially expressed genes. In the future, mouse lines like the Cx3cr1-CreERT2 (129, 130) for monocytes/macrophages or Tmem119-CreERT2 (131) and Tmem119-tdTomato reporter (132) for resident microglia may be favored because they have not been shown to have similar caveats to date.
Another limitation to our study is that we analyzed bulk RNA samples. It is possible that subtle changes in myeloid gene expression were masked by dilution with RNA from other cell types. On the other hand, mechanical dissociation of myeloid cells has other caveats, such as the possibility that diseaseassociated microglia or microglia adjacent to amyloid deposits are more fragile and difficult to isolate. This study intended to understand how myeloid Arg1 insufficiency impacted overall CNS gene expression in neurodegeneration. Future studies should increase replication with a larger sample size and compare these targeted transcriptome results with larger transcriptome dataset to ensure the effects of myeloid Arg1 deficiency during amyloidosis.
Overall, our findings suggest that myeloid Arg1 haploinsufficiency elevates Ab associated genes enriched in brain myeloid cells and oligodendrocytes. Deficiency of Arg1 in brain myeloid cells preferentially promotes a transcriptomic signature that is more homeostatic and less phagocytic, possibly inhibiting their crucial transition from a homeostatic to a disease-associated state during amyloidosis, leading to more Ab deposition. Future therapeutics to modulate arginase 1 in brain myeloid cells may provide potential disease-modifying treatment for AD patients.

DATA AVAILABILITY STATEMENT
The datasets generated for this study are included in the supplemental documents and are also available from the corresponding author upon request. The raw nCounter data are publicly available through the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) under accession number GSE172108.

ETHICS STATEMENT
The animal study was reviewed and approved by Institutional Animal Care and Use Committee (IACUC) in the University of South Florida and the University of Kentucky.

AUTHOR CONTRIBUTIONS
CM contributed to the design and implementation of the research, performed bioinformatic analyses with nSolver, IPA and STRING, statistical analysis using SPSS, interpreted data, and wrote the first draft of the manuscript. JH contributed to the breeding of mice, harvesting mouse brains, and statistical analysis using SPSS. AK, HL, and M-LS contributed to the mRNA extraction and preparation. MG contributed to the breeding and genotyping of the mice. MO, BZ, JG, and DF contributed to the characterization of mouse lines. MG, DM, PB, and DL contributed to the design, conceptualization of the research, interpretation of the data, and writing of the manuscript. All authors contributed to the article and approved the submitted version.

ACKNOWLEDGMENTS
PB was affiliated with James A. Haley Veterans Affairs Hospital and would like to acknowledge this affiliation.

SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2021. 628156/full#supplementary-material Supplementary Figure 1 | Overview of all normalized data. All data were normalized for gene transcript expression against APP transgene genotype and Arg1 haploinsufficiency genotype. (A) A heat map of all normalized data via unsupervised clustering of mouse genotypes and a condensed heat map for unsupervised clustering of 770 genes. All data passed QC metrics without any flags (top bar, yellow). Genes expressed below the background threshold are flagged in blue. The orange or blue color in the heat map indicates high or low gene expression z-score of each sample. All scores are presented on the same scale via a ztransformation. (B) Principal components of all normalized data. The principal component analysis shows the four groups' variance using the four leading components based on all normalized data. (C) A heat map of correlation matrix of all signature pathways from pathway scoring analysis was presented. Yellow and blue colors indicate positive and negative correlation and thus aggregate separately. Pathways that show statistically significant main APP transgene genotype effects are highlighted in red or blue text to indicate up or down-regulation, respectively.