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.

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Published in Frontiers in Immunology, v. 12, article 628156.

© 2021 Ma, Hunt, Kovalenko, Liang, Selenica, Orr, Zhang, Gensel, Feola, Gordon, Morgan, Bickford and Lee

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Funding Information

Funding for this work was provided by the NIH R21-AG055996 (to DL), R01-AG054559 (to DL), R01-AG051500 (to DM), R01-NS091582 (to JG), R01-AI095307 (to DF), Alzheimer’s Association AARGD-16-441534 (to DL), and MNIRGD-12-242665 (to DL), Florida Department of Health Ed and Ethel Moore Alzheimer’s disease (8AZ30) (to DL and PB), and IKBX004214 (to PB). CM was awarded by USF Health Neuroscience Institute Dorothy Benjamin Graduate Fellowship in Alzheimer’s Disease.

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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.

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2021.628156/full#supplementary-material

The supplementary material is also available for download as the additional files listed at the end of this record.

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 z-transformation. (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.