JNEUROSCI.1563-23.2024.full (1)-html.html

Imbalance in glucose metabolism regulates the transition of microglia from homeostasis to

Disease Associated Microglia stage 1

Yuxi Liu

, Witty Kwok

, Hyojung Yoon

, Jae Cheon Ryu

, Patrick Stevens

, Tara R. Hawkinson

6, 7

Cameron J. Shedlock

6, 7

, Roberto A. Ribas

6, 7

, Terrymar Medina

6, 7

, Shannon B. Keohane

6, 7

, Douglas

Scharre

, Lei Bruschweiler-Li

, Rafael Bruschweiler

, Alban Gaultier

, Karl Obrietan

, Ramon C. Sun

, and Sung Ok Yoon

Department of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, OH,

USA.

Department of Chemistry and Biochemistry

The Ohio State University, Columbus, OH, USA.

Department of Neuroscience, The Ohio State University, Columbus, OH, USA.

Department of Neurology, The Ohio State University, Columbus, OH, USA.

Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.

Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida,

Gainesville, FL, USA.

Center for Advanced Spatial Biomolecule Research, University of Florida, Gainesville, FL, 32610,

USA

Center for Brain Immunology and Glia, University of Virginia, Charlottesville, VA, 22908, USA

*Corresponding author, Sung Ok Yoon, PhD

184 Rightmire Hall

Department of Biological Chemistry and Pharmacology

1060 Carmack Rd.

Ohio State University

Columbus, OH 43210

Email:

Sung.yoon@osumc.edu

JNeurosci Accepted Manuscript

Number of pages, 57

Number of figures, 12

Number of words for abstract, 188

Number of words for introduction, 1,091

Number of words for discussion, 1,532

Conflict of interest, The authors declare no competing financial interests.

Acknowledgments, The current study was supported by grants to SOY from NIH (R01AG055059,

R01NS107456, R01DK120108), and grants from NIH to RCS (5 R01AG066653, and 5 R01

AG078702). In addition, S10 OD026842 instrument grant and Genomics Shared Resource grant

(NCI CCSG grant, P30CA016058) to Ohio State University.

ABSTRACT

Microglia undergo two-stage activation in neurodegenerative diseases, known as disease-associated

microglia (DAM). TREM2 mediates the DAM2 stage transition, but what regulates the first DAM1

stage transition is unknown. We report that glucose dyshomeostasis inhibits DAM1 activation, and

PKM2 plays a role. As in tumors, PKM2 was aberrantly elevated in both male and female human AD

brains, but unlike in tumors, it is expressed as active tetramers, as well as among TREM2

microglia

surrounding plaques in 5XFAD male and female mice. snRNAseq analyses of microglia without

Pkm2

in 5XFAD mice revealed significant increases in DAM1 markers in a

distinct ‘metabolic’ cluster, which

is enriched in genes for glucose metabolism, DAM1, and AD risk. 5XFAD mice incidentally exhibited

a significant reduction in amyloid pathology without microglial

Pkm2

. Surprisingly, microglia in 5XFAD

without

Pkm2

exhibited increases in glycolysis and spare respiratory capacity, which correlated with

restoration of mitochondrial cristae alterations. In addition, in situ spatial metabolomics of plaque-

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SIGNIFICANCE STATEMENT

Although reduced glucose uptake in the brain has been recognized as one of the earliest pathological

events that accompany

Alzheimer’s disease (AD), it has not been clear whether this was due to a

brain-wide defect in glucose metabolism or a dysfunction in a particular cell type. Our data suggest

that dysregulation of metabolic homeostasis in microglia is critical for global AD pathology in a mouse

model. Upon restoring glucose metabolism in microglia genetically, AD pathology is attenuated with

a concomitant increase in DAM genes, especially DAM1 genes. These results suggest that this

increase in DAM gene expression is protective against AD pathology, and glucose dyshomeostasis

is a trigger to inhibit expression of protective DAM genes in AD.

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INTRODUCTION

Brain hypometabolism is one of the pathological events that accompany

Alzheimer’s disease (AD)

(Stanley et al., 2016). In particular, selective reduction in

F-fluorodeoxyglucose (FDG) uptake in

vulnerable areas of the brain in presymptomatic

ApoE4

homozygous AD patients has been reported

(Kennedy et al., 1995; Reiman et al., 1996), indicating brain hypometabolism is one of the earliest

dysfunctions in AD. More recently, large-scale proteomic analyses of >2,000 human AD brains and

~400 cerebrospinal fluid (CSF) samples ide

ntified the ‘sugar metabolism’ module as the most

significant module to be associated with AD pathology and cognitive decline out of 13 distinct modules

that included the synapse/neuron module (Johnson et al., 2020)

. In this ‘sugar metabolism’ module,

several glycolytic gene products were included, such as glyceraldehyde-3-phosphate dehydrogenase

(GAPDH),



-enolase (ENO1), pyruvate kinase muscle isoform (PKM), and lactate dehydrogenase b

(LDHb), and of those, PKM and LDHb proteins were also detected in CSF samples. PKM and LDHb

protein levels positively correlated with AD progression, highlighting the role of glucose metabolism

in AD pathology. The major cell types represented in the

‘sugar metabolism’ module are microglia

and astrocytes, suggesting that loss of control of glucose and carbohydrate metabolism in microglia

and astrocytes plays a critical role in AD pathology.

Once taken up, glucose is metabolized through glycolysis and then oxidative phosphorylation via the

TCA cycle to generate ATP. In metabolically active differentiated cells, oxidative phosphorylation

dominates by virtue of generating higher levels of ATP than glycolysis per glucose unit (Vander

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Heiden et al., 2009; Van den Bossche et al., 2017). Actively proliferating cancer cells and immune

cells on the other hand are known to undertake metabolic reprogramming in response to changes in

metabolic demand or energy availability. A key molecule in this metabolic process is PKM2. PKM2

is the alternatively spliced isoform of PKM pre-mRNA, where PKM1 includes exon 9, while PKM2

includes exon 10 in a mutually exclusive manner (David et al., 2010; Chen et al., 2012). PKM2 is

mainly expressed among proliferating cells in embryonic tissues, but its expression is often induced

when cells resume proliferation during regeneration or tumor formation in the adult (Mazurek, 2011;

Lunt et al., 2015). PKM2 is allosterically regulated by fructose-1,6-bisphosphate binding, switching

100

between its inactive monomeric/dimeric form and its active tetrameric form, unlike PKM1 that exists

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as stable tetramers with high pyruvate kinase activity (Dayton et al., 2016). When induced in

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proliferating tumors, PKM2 is often tyrosine phosphorylated at Y105 in response to growth factor

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signaling (Hitosugi et al., 2009), resulting in inhibition of metabolically active tetramer formation. Such

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aberrant induction of enzymatically inactive PKM2 monomers/dimers in tumors leads to aerobic

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glycolysis with increased lactate production, termed the Warburg effect, where glycolytic metabolites

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are diverted from the TCA cycle to produce nucleotides, proteins, and lipids that are needed for

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generating daughter cells via proliferation at the expense of ATP production (Vander Heiden et al.,

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2009; Vander Heiden et al., 2011). In addition, PKM2 dimers can be translocated to the nucleus,

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activating transcription of glycolytic genes thereby regulating aerobic glycolysis (Yang et al., 2011;

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Gao et al., 2012).

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Besides in tumors, PKM2 was shown to be induced in immune cells, such as macrophages and T

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cells, wherein metabolic pathways are known to regulate their phenotype and function in addition to

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providing energy (Van den Bossche et al., 2017; Geltink et al., 2018). For instance, as a dimeric form,

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nuclear PKM2 was shown to activate signal transducer and activator of transcription 3 (STAT3) to

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induce pro-inflammatory cytokines, such as IL-6, IL-1



, and IL-17 in T cells, in part by increased

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lactate production (Pucino et al., 2019; Angiari et al., 2020). In contrast, when PKM2 is stabilized as

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a tetrameric form pharmacologically with DASA-58 or TEPP-46, IL-17 production is reduced in T cells

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under inflammatory conditions (Angiari et al., 2020; Seki et al., 2020). These results suggest that

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activation of the glycolytic pathway is critical for maintaining pro-inflammatory macrophages, while

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anti-inflammatory macrophages employ oxidative phosphorylation instead.

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Microglia belong to a myeloid lineage and are known to undergo metabolic changes in

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neurodegenerative diseases, for instance, via triggering receptor expressed on myeloid cells 2

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(TREM2)

(Kleinberger et al., 2017; Ulland et al., 2017; Parhizkar et al., 2023). PKM2 expression in

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microglia in vivo has been reported under neuropathological conditions (Romero-Ramirez et al., 2022;

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Li et al., 2023), including human AD cases and in 5XFAD mice (Pan et al., 2022). In particular, Pan

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et al. reported that PKM2 in microglia of 5XFAD mice promotes aerobic glycolysis by increasing

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lactate production, thereby modifying lactylation of histones, which in turn activates glycolytic genes

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(Pan et al., 2022). These actions presumably rely on monomeric/dimeric forms of PKM2 that favor

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aerobic glycolysis over oxidative phosphorylation producing lactate as in tumors. Here, we report that

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in addition to promoting aerobic glycolysis most likely as dimers as Pan et al. described (Pan et al.,

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2022), PKM2 also influences the respiratory capacity of microglia in 5XFAD mice as tetramers. A

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tetrameric form of PKM2 is highly expressed in brain tissues from human AD patients and 5XFAD

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mice. When PKM2 was activated with TEPP-46 to form tetramers in primary microglia, microglia

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responded by decreasing glycolysis and oxidative phosphorylation. Deleting

Pkm2

from microglia

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using the same Cx3cr1-CreERT2 line as Pan et al. in contrast increased both glycolytic capacity and

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maximal respiration. These results suggest that the metabolic effects of

Pkm2

deletion are not

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homogeneous in one pathway, but includes multiple distinct pathways, such as alteration in

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respiratory responses and glycolytic output, which together coordinate to control overall metabolic

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needs in microglia. In line with this notion, mitochondrial cristae in microglia in 5XFAD mice exhibit

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defects, which become restored with microglial

deletion of

Pkm2

. In addition, metabolomics data from

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our in situ spatial metabolomics study illustrated that

Pkm2

deletion in microglia of 5XFAD mice led

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to increased mitochondrial respiratory activity with minor changes in glycolytic metabolites. In parallel

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with both glycolytic and respiratory metabolic changes induced by

Pkm2

deletion, expression of many

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DAM1 stage genes was increased in select metabolic cluster(s) of microglia upon

Pkm2

deletion.

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These data together suggest that the imbalance in glucose metabolism initiated by aberrant PKM2

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activation as tetramers in microglia is a critical factor in regulating transition from homeostasis to

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DAM1 stage. Along with a recent report, in which deleting hexokinase 2 (HK2), the first enzyme in

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the glycolytic pathway from microglia, also caused a reduction in both glycolytic and respiratory

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capacity (Hu et al., 2022), our results suggest that perturbations in the glycolytic pathway will have an

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impact not only on glycolysis but also oxidative phosphorylation under neuropathological conditions.

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MATERIALS AND METHODS

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Antibodies.

Antibodies used for the study include; PKM1 (Cat#7067, RRID:AB_2715534), PKM2

157

(Cat#4053,

RRID:AB_1904096),

HK1

(Cat#2024,

RRID:AB_2116996),

HK2

(Cat#2867,

158

RRID:AB_2232946),

GAPDH

(Cat#5174,

RRID:AB_10622025),

LDHA

(Cat#2012,

159

RRID:AB_2137173), PDH (Cat#3205, RRID:AB_2162926), PDHK1 (Cat#3820, RRID:AB_1904078),

160

PFKFB3 (Cat#13123, RRID:AB_2617178), PSD95 (Cat#3450, RRID:AB_2292883), A



(NAB228,

161

Cat#2450, RRID:AB_490857), p-AMPK



(Cat#2535, RRID:AB_331250), AMPK



(Cat#5831,

162

RRID:AB_10622186),

TREM2

(Cat#76765,

RRID:AB_2799888),



-actin

(Cat#3700,

163

RRID:AB_2242334), and APOE (Cat#49285 and Cat#68587) were from Cellsignaling Technology.

164

PFKP (Cat#13389-1-AP, RRID:AB_2252278) and A



(6E10, SIG-39320-1000, RRID:AB_662798)

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were from Proteintech and Covance, respectively. Iba1 (Cat# ab5076-100, RRID:AB_2224402) from

166

Abcam, GFAP (Cat#MAB360, RRID:AB_11212597) from Millipore, AmyloGlo (Cat#TR-300-AG) from

167

Biosensis, vGlut1 and TREM2 for IHC from Drs Julia Kaltschmidt and Christian Haass, respectively.

168

Secondary antibodies were from ThermoFisher (Cat#A31572, RRID:AB_162543; S21381,

169

RRID:AB_2307336; A11055, RRID:AB_2534102; SA1011; A11029, RRID:AB_2534088; A10525,

170

RRID:AB_2534034) or from Jackson ImmunoResearch Labs (Cat#305-035-045, RRID:AB_2339403;

171

711-035-152,

RRID:AB_10015282;

705-065-147,

RRID:AB_2340397;

712-165-150,

172

RRID:AB_2340666). Topro-3 was from Thermofisher (Cat#T3605).

173

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Other reagents.

Quantikine ELISA human amyloid

β (aa 1-42) immunoassay kit (R&D systems,

175

Cat#DAB142), Lactate-Glo Assay kit (Promega, Cat#J5021), Adult brain dissociation kit (Miltenyi,

176

Cat#130-107-677), Cd11b/c conjugated beads for rat (Miltenyi, Cat#130-105-634), Cd11b conjugated

177

beads for mouse (Miltenyi, Cat#130-093-634), DSS (Thermo Fisher, Cat#A39267), TRIzol Reagent

178

(Invitrogen, Cat#15596-018), PowerUp SYBR Green Master Mix (Applied Biosystems, Cat#A25778),

179

Protector RNase inhibitor (Roche, Cat#03335399001), OneTaq RT-PCR kit (New England Biolabs,

180

Cat#E5310S), Slowfade Diamond Antifade Mountant (Thermo Fisher, Cat#S36972), TEPP-46

181

(MedChemExpress, Cat#HY-18657), XF24 cell culture microplate (Agilent, Cat#100777-004), XFe24

182

FluxPak (Agilent, Cat#102340-100), XF Calibrant (Agilent, Cat#100840-000), XF basal media

183

(Agilent, Cat#102353-100), 1M Glucose solution (Agilent, Cat#103577-100), 100 mM Pyruvate

184

solution (Agilent, Cat#103578-100), 200 mM Glutamine solution (Agilent, Cat#103579-100), 2-

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deoxyglucose (Cayman, Cat#14325), Glucose (Sigma, Cat#G7528), Oligomycin (Millipore,

186

Cat#495455), FCCP (Cayman, Cat#15218), Rotenone (Cayman, Cat#13995), MEM (Gibco,

187

Cat#61100-061), DMEM (Gibco, Cat#12800-017), Fetal bovine serum (Gibco, Cat#10437-028),

188

Penicillin and streptomycin (Gibco, Cat#15140-122), Trypsin (Gibco, Cat#15090-046), DNase I

189

(Worthington, Cat#LS002147),

40 μm Flowmi Tip Strainer (Bel-Art, Cat#H13680-0040), all other

190

chemicals were from Sigma.

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192

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Table 1; Primers

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Name

Forward primers

Reverse primers

Mouse Gapdh

AGCTTGTCATCAACGGGAAG

TTTGATGTTAGTGGGGTCTCG

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Mouse Actb

GGCTGTATTCCCCTCCATCG

CCAGTTGGTAACAATGCCATGT

Mouse P2ry12

ATGGATATGCCTGGTGTCAACA

AGCAATGGGAAGAGAACCTGG

Mouse Cx3cr1

GAGTATGACGATTCTGCTGAGG

CAGACCGAACGTGAAGACGAG

Mouse Apoe

TGTTTCGGAAGGAGCTGACT

TGTGTGACTTGGGAGCTCTG

Mouse Trem2

AGGGCCCATGCCAGCGTGTGGT

CCAGAGATCTCCAGCATC

Mouse Tyrobp

GAGTGACACTTTCCCAAGATGC

CCTTGACCTCGGGAGACCA

Mouse Cstb

AGGTGAAGTCCCAGCTTGAAT

GTCTGATAGGAAGACAGGGTCA

Mouse Timp2

TCAGAGCCAAAGCAGTGAGC

GCCGTGTAGATAAACTCGATGTC

Mouse Cd33

CCGCTGTTCTTGCTGTGTG

AAGTGAGCTTAATGGAGGGGTA

Mouse Clu

CGAAGATGCTCAACACCTCA

TGTGATGGGGTCAGAGTCAA

Mouse Adam10

AGCAACATCTGGGGACAAAC

TTGCACTGGTCACTGTAGCC

Mouse Mef2c

CGGTGTCGTCAGTTGTATGG

TGCAGTAGATATGCGGCTTG

Mouse Sorl1

CCTGTCGCGTTTTTCTTAGC

TTCTGGCAATGCACTTCTTG

Rat Gapdh

GTATTGGGCGCCTGGTCACC

CGCTCCTGGAAGATGGTGATGG

Rat Trem2

AAGATGCTGGAGACCTCTGG

GGATGCTGGCTGTAAGAAGC

Rat Apoe

CTGGTTCGAGCCGCTAGTG

CCTGTATCTTCTCCATTAGGTTTGC

Human tissue samples.

Tissues from the frontal cortex were obtained through the UCSD

195

Experimental Neuropath Laboratory (RRID:SCR_114906). The ages of AD and control cases ranged

196

from 71 to 91 and 69 to 97 years, respectively. The Braak scores for AD cases were 6.2 to 6.3 and

197

for control cases were 0.0 to 3.0.

198

199

Transgenic

mice.

All

mice

were

obtained

from

Jackson

Lab:

5XFAD

(#34840,

200

RRID:MMRRC_034840-JAX)

(Oakley

al.,

2006),

Cx3cr1-CreERT2

(#021160,

201

RRID:IMSR_JAX:021160) (Parkhurst et al., 2013), PKM2fl/fl (Israelsen et al., 2013) (#024048,

202

RRID:IMSR_JAX:024048), and Cx3cr1-GFP (#005582, RRID:IMSR_JAX:005582) (Jung et al., 2000).

203

5XFAD from Jackson Lab in B6/SJL F1 hybrid background was made congenic by backcrossing 10

204

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generations with C57/BL6. To generate microglial deletion of PKM2 in 5XFAD background, 5XFAD

+/-

205

:Cx3cr1-CreERT2

+/-

:PKM2

fl/+

mice were bred with C57/BL6:Cx3cr1-CreERT2

-/-

:PKM2

fl/+

mice to obtain

206

the experimental 5XFAD

+/-

:Cx3cr1-CreERT2

+/-

:PKM2

fl/fl

and the control 5XFAD

+/-

:Cx3cr1-CreERT2

+/-

207

:PKM2

+/+

mice. For brevity, the experimental mice are designated as FAD:i-PKM2

M∆

and the control

208

as FAD: i-PKM2

. PKM2 was deleted from microglia at 2-months via daily oral gavage for 5 days

209

with 75 mg/kg of tamoxifen dissolved in 10% ethanol and 90% oil. All experimental mice were housed

210

in an animal facility with ad libitum access to food and water on a 12/12 hr light/dark cycle. All animal

211

protocols were approved by the Institutional Animal Care and Use Committee (IACUC) at the Ohio

212

State University. Both males and females were used at the same time throughout the project.

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ELISA.

Mice were transcardially perfused with ice-cold saline. The isolated brain hemispheres were

215

homogenized with a Dounce homogenizer in 1 ml per 0.1 g tissue of Solution A containing 0.32 M

216

sucrose, 10 mM HEPES (pH 7.4), 2 mM EDTA, supplemented with 1 mM sodium orthovanadate, 10

217

β-glycerophosphate, 10 μg/ml leupeptin, 1 μg/ml aprotinin, and 1 mM PMSF. Homogenates were

218

supplemented with 1 ml per 0.1 g tissue of 0.2% diethylamine (DEA) in 50 mM NaCl and centrifuged

219

at 100,000 G, 4

C for 1 hour. The supernatant was neutralized with 1/10 volumes of 0.5 M Tris (pH

220

6.8) to obtain the

soluble fraction. The pellet was resuspended in 100 μL of 70% formic acid and

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sonicated 3 times for 30s. Then the mixture was neutralized with 15 volumes of 1.5 M Tris (pH 8.8)

222

to obtain the insoluble fracti

on. Aβ42 levels in the soluble and insoluble fractions were measured

223

using the Quantikine ELISA Human Amyloid β (aa1-42) Immunoassay kit (R&D Systems, Cat#

224

DAB142)

according to the manufacturer’s instructions.

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Amylo-Glo staining.

To detect Aβ plaques in the mouse brains, Amylo-Glo staining was done

227

according to the manufacturer’s instructions (Biosensis Cat#TR-300-AG). Briefly, mouse brain

228

sections were treated with 70% ethanol for 5 min, followed by water for 2 min. Then sections were

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incubated with 1X Amylo-Glo for 10 min and rinsed with saline for 5 min followed by water for 15

230

sec. After that, sections were subjected to immunohistochemical analysis.

231

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Lactate measurement

233

Rat primary microglia were treated with TEPP-

46 (75 μM) for 1 hour. Then the culture media were

234

collected. Lactate levels were measured using the Lactate-Glo Assay kit (Promega, #J5021)

235

according to the manufacturer’s instructions.

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Immunohistochemistry of mouse brain tissues.

Mice were transcardially perfused with 3%

238

paraformaldehyde in 0.1 M phosphate buffer (pH 7.2). The isolated brains were post-fixed in the same

239

buffer for 2 hrs at room temperature prior to cyroprotection in 20% sucrose in 0.1 M phosphate buffer

240

at 4

C for 24h. The brains were then sectione

d at 30 μm on the coronal plane and collected as floating

241

sections. For immunohistochemistry, sections containing the hippocampus and cortex (Bregma -1.94

242

mm) were first blocked for 2h at room temperature with 10% horse serum, 1% bovine serum albumin

243

(BSA), and 0.3% Triton X-100 in 1X Tris-buffered saline (TBS), and incubated with primary antibodies

244

in the incubation buffer containing 5% horse serum, 0.5% BSA, and 0.3% Triton X-100 in 1X TBS

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overnight. Antibody binding was detected by incubating sections with fluorescently labeled secondary

246

antibodies for 2h in the incubation buffer. After staining nuclei with Topro-

3 (0.2 μM), sections were

247

mounted in Slowfade Diamond Antifade Mountant (Thermofisher), and imaged either on Zeiss LSM

248

900 Airyscan 2-point scanning confocal microscope (RRID:SCR_022263) or Andor revolution WD

249

spinning disk confocal microscope. For quantification and analyses, FIJI software was utilized. For

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imaging and quantification of vGlut1

and PSD95

puncta, the spatial overlap between vGlut1 and

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PSD95 were measured using Synapse Counter, as described previously (Dzyubenko et al., 2016), in

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addition to analyzing whether the overlap was on the same plane through orthogonal views.

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Western blotting.

Primary microglia and fresh brain tissues were homogenized in lysis buffer

255

containing 1% NP-40, 10% glycerol, 20 mM Tris (pH 8.0), 137 mM NaCl, 1 mM MgCl

, 0.5 mM EDTA,

256

10 mM sodium pyrophosphate, supplemented with 1 mM sodium orthovana

date, 10 mM β-

257

glycerophosphate, 10 μg/ml leupeptin, 1 μg/ml aprotinin, and 1 mM PMSF. Homogenates were

258

rotated at 4

C for 15 min and centrifuged at 14,000 rpm, 4

C for 10 min, and supernatants were

259

collected as lysates. Western blotting was done as previously described (Yoon, 2012).

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Crosslinking of PKM2 proteins.

Protein lysates were subjected to crosslinking as described

262

(Palsson-McDermott et al., 2015). Briefly, lysates from primary microglia and brain tissues were

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incubated with 0.5 mM DSS at room temperature for 30 min. The reaction was quenched with 50 mM

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Tris (pH 7.5) at room temperature for 15 min. Crosslinked lysates were resolved on 5-20%

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polyacrylamide gradient gels and subjected to Western blotting analyses using PKM2 antibody.

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Two-photon live imaging of microglial recruitment in vivo.

Cx3cr1-GFP mice were subjected to

268

surgery to create a cranial window when they were 3-4 months old. To reduce inflammation and pain,

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ibuprofen (5 mg/kg in drinking water) and buprenorphine (0.05 mg/kg, SC) were administered to mice

270

for a day before the surgery. In addition, on the day of the surgery, carprofen (5 mg/kg, SC) was

271

administered to reduce edema and inflammation.

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Mice were anesthetized under an isoflurane/oxygen mixture (5% for induction and 2% for

273

maintenance), and the isoflurane flow was controlled by scavenging vaporizer (Cat #: 72-6468,

274

RRID:SCR_018956, Harvard Apparatus, Holliston, MA, USA). Throughout the surgery, body

275

temperature was maintained with a heating pad, and an eye ointment was applied to prevent dryness

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and provide protection from other solutions used during the surgery. Animals were placed onto the

277

stereotactic table (Cartesian Research, Sandy, OR, USA) and secured into position. Fur was removed

278

to expose the scalp which was then disinfected three times using independent swabs with 70% EtOH

279

and betadine. An incision was made to expose skull and periosteum was removed. A circular

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craniotomy of about 3 mm was made with high-speed drill at the position of AP: -2.00 mm, ML: 1.7

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mm. No.1 coverglass (Harvard Apparatus) was glued above the craniotomy with Vetbond (3M, St.

282

Paul, MN, USA) and dental cement. A custom-made plastic head bar was secured to the skull with

283

the dental cement to immobilize the head during imaging. Ibuprofen was provided in drinking water

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for 2-3 days post-op. Mice were allowed to recover from the operation for a minimum of 2 weeks prior

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to imaging.

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For two-photon laser stimulation and in-vivo imaging, mice with cranial windows were treated

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daily for 4 days via oral gavage with either vehicle (0.5% carboxymethyl cellulose, 0.1% Tween 80)

288

or TEPP-46 at 50 mg/kg. On the 5

day, mice were anesthetized with an isoflurane/oxygen mixture

289

(5% induction and 1.5% maintenance) and placed onto a stainless-steel frame with the head

290

immobilized. The stainless-steel frame was then secured to a motorized stage using a FVMPE-RS

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multiphoton microscope system (Olympus Corporation, Tokyo, Japan). With a 25X water immersion

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objective (Cat #: XLPLN25XWMP2, Olympus), blood vessels on the cortical surface of the brain were

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visualized through the cranial window. Cortical cells ~50 µm below the surface was imaged with a

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Ti:Sapphire laser (Chameleon Vision II, Coherent, Santa Clara, CA, 44USA). The region of interest

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(ROI) circle was drawn and irradiated with a 700 nm laser at 100% power for 1 second. The time

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series of images were acquired every 1 min for 1 hour using a GaAsP photomultiplier tube through a

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yellow dichroic filter (FV30-FCY, Olympus) at a 920 nm wavelength.

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Images were analyzed as described in Etienne et al. (Etienne et al., 2019) with open-source

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analysis platform ImageJ and Icy (de Chaumont et al., 2012). Images were first processed with

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MultiStackReg plugin in ImageJ (Align, Transformation: Rigid body) for correction of drifts. The

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modified images were then imported into Icy and a 35 µm circle was drawn, centered on the lesion

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site. The signal was measured as mean intensity with ROI intensity evolution plugin over time and

303

measured for 1 hour. The measured value was then exported as .XLS file and normalized with

304

equation: R(

) = (R

inner

(

) - R

inner

(

))/ R

outer

(

) according to (Davalos et al., 2005). Each value from the

305

JNeurosci Accepted Manuscript

time points were normalized by the mean of baseline value. Normalized value was statistically tested

306

with paired t-test (two-tailed, p<0.0001) using GraphPad Prism (GraphPad Software, La Jolla, CA,

307

USA).

308

309

Primary microglia cultures.

Brains from Sprague Dawley rat pups at postnatal day 1-2 were isolated

310

and dissected to obtain cortices and hippocampi in ice-cold 120 mM NaCl, 2.5 mM KCl, 10 mM

311

NaHCO

, 1 mM NaH

, 20 mM glucose, 20 mM HEPES. Following 20 min digestion at 37

℃

with

312

0.25% trypsin and 2.5 mg/mL DNase I, tissues were triturated with a fire-polished glass pipette in

313

minimum essential media (MEM) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin

314

and streptomycin (P/S). Single cells from two brains were plated on one poly-D-Lysine coated T-75

315

flask, and the culture medium was changed once every third day and thereafter. On days 10-11 in

316

vitro (DIV10-11), microglia were isolated by a shake-off method using a shaker at 150 rpm, 37

C for

317

4h. Isolated microglia were subsequently immuno-purified using CD11b/c antibody conjugated beads,

318

(Miltenyi, Cat# 130-105-634, RRID:AB_2783886) and plated at 0.3-0.8 x 10

cells/mL on PDL-coated

319

dishes or coverglasses in Dulbecco’s modified Eagle’s medium (DMEM) with 10%FBS and 1%P/S.

320

Purified microglia were typically cultured for 3-4 days (37

C, 5%CO

) prior to experimentation.

321

322

Metabolic stress treatment.

Rat primary microglia were plated in 6- or 12-well plates at a density

323

of 0.7-0.8 x 10

/well or 0.3-0.4 x 10

/well, respectively. Two to three days after plating, microglia

324

JNeurosci Accepted Manuscript

were treated with TEPP-

46 (75 μM), 2-DG (1 mM), IACS-010759 (100 nM), dichloroacetate (DCA, 5

325

mM) for 24 hrs or indicated time points.

326

327

MALDI-MSI sample preparation and data export.

Slides were prepared as previously described,

328

(Hao et al., 2021), in brief, High-performance liquid chromatography (HPLC)-methanol, HPLC-grade

329

water and N-(1-Naphthyl) ethylenediamine dihydrochloride (NEDC) matrix were obtained from Sigma-

330

Aldrich. Tissue was slow frozen over a bath of dry-ice cold 2-Methylbutane for 7 minutes prior to

331

storage in -80C until cutting on slides. Brains were cut on a Lecia CM1860 cryostat at 10



M onto

332

positively charged glass slides and stored in the -80 until the day of sample preparation. On day of

333

run, the slides were removed from the freezer and placed in a desiccatior for one hour. Slides were

334

immediately sprayed with 14 passes of 7mg/mL NEDC matrix in 70% methanol, applied at

335

0.06mL/min with a 3mm offset and a velocity of 1200mm/min at 30°C and 10psi using the M5 Sprayer

336

with a heated tray of 50°C. Slides were used immediately or stored in a desiccator until use. For the

337

detection of metabolites, a Bruker timsTOF QTOF high-definition mass spectrometer was used. The

338

laser was operating at 10000 Hz with 60% laser energy, with 300 laser shots per pixel and spot size

339

of 50µm at X and Y coordinates of 50µm with mass range set at 50

– 2000m/z in negative mode. Data

340

acquisition spectrums were uploaded to Scils Software (Bruker Corporation) for the generation of

341

small molecules. Regions of interest (ROIs) were drawn around brain regions based on the Allen

342

Brain Atlas and exported using the SCILs lab API as tabular format. Pixel to pixel information and

343

statistical analysis are performed using GraphPad Prism9 (RRID:SCR_002798). The data are

344

JNeurosci Accepted Manuscript

represented as

relative abundance is the area under the curve for each mass spec peak and

345

normalized to total ion current.

346

347

Microglia isolation from adult mouse brain for snRNAseq and protein analyses.

Mice were

348

transcardially perfused with ice-cold saline. Brains were isolated and dissociated using the Adult Brain

349

Dissociation Kit (Miltenyi, Cat#130-107-677). After dissociation, the cells were triturated with a fire-

350

polished glass pipette in HEBSS solution (120 mM NaCl, 2.5 mM KCl, 10 mM NaHCO

, 1 mM

351

NaH

, 20 mM glucose, 20 mM HEPES)

and filtered through a 40 μm cell strainer. The cell

352

suspension was then centrifuged at 300 rcf for 5 min at 4

C. The cell pellet was resuspended with

353

37% Percoll and subjected to a Percoll gradient (70%, 37%, 30%, 0%) at 4

C, and was centrifuged

354

for 40 min at 300 g (Lee and Tansey, 2013). Microglia were collected at the 70%-37% interphase,

355

washed with 12 mL HEBSS and then immuno-purified using CD11b antibody-conjugated microbeads

356

(Miltenyi, Cat#130-093-634). For Western blotting, proteins were extracted with lysis buffer

357

(Harrington et al., 2002), and snap frozen in liquid N2 and stored at -80

C until analyses. For single

358

nucleus RNA sequencing, the purified microglia were resuspended with 10% DMSO/90% fetal bovine

359

serum (FBS) and cryopreserved in liquid nitrogen until all mice were harvested.

360

361

Single nucleus RNA sequencing of microglia.

Single nuclei were isolated from microglial cell

362

suspensions according to the 10X Genomics Sample Preparation Demonstrated Protocol. Briefly, cell

363

suspension was centrifuged at 300 rcf for 5 min, and the cell pellet was resuspended with 1 ml Lysis

364

JNeurosci Accepted Manuscript

Buffer (10 mM Tris-HCl, 10 mM NaCl, 3 mM MgCl

, and 0.1% NP40 in nuclease-free water) using a

365

wide-bore pipette tip. Then the cells were lysed on ice for 5 min and centrifuged at 500 rcf for 5 min

366

at 4

C. The nuclei pellet was washed with 1 mL Nuclei Wash and Resuspension Buffer (1X PBS with

367

2% BSA and 0.2 U/μL RNase inhibitor) for 2 times using a regular-bore pipette tip. After the second

368

wash, the nuclei pellet was resuspended with an appropriate volume of Nuclei Wash and

369

Resuspension Buffer to achieve a concentr

ation of 1000 nuclei/μL and filtered through a 40 μm

370

Flowmi Tip Strainer. The nuclei concentration was determined using a hemocytometer and adjusted

371

to 1000 nuclei/μL. Then the nuclei were then subjected to chip loading immediately using the 10X

372

Genomics C

hromium Next GEM Single Cell 3’ Reagent kits v3.1 at Ohio State University Genomics

373

Shared Resource.

374

375

Single-nucleus RNA-seq data processing and alignment.

Sequenced single cell reads in FASTQ

376

format were aligned to a reference transcriptome using the GRCm38 (mm10) genome (downloaded

377

March 2021 refdata-gex-mm10-2020-A build from 10x Genomics website) using 10x Genomics Cell

378

Ranger software (Cell Ranger 6.0.0 (Zheng et al., 2017)) based on default parameters with include-

379

introns function. Output files from Cellranger count was analyzed using Seurat 4.0.4 (Hao et al., 2021).

380

Briefly, QC-filtered cells were further selected based on RNA content thresholds: greater than 200;

381

less than 4000 unique expressed genes (nFeature_RNA) and mitochondrial gene content less than

382

20%. Preprocessed data from the 6 samples were integrated using the standard processing with

383

Seurat. Data was normalized using log1p via the NormalizeData function (Hao et al., 2021). Variable

384

features were found with the FindVariableFeatures function (2000 features). After scaling and PCA

385

JNeurosci Accepted Manuscript

normalization with default parameters, cells were partitioned into clusters using Seurat functions

386

FindNeighbors and FindClusters functions (dimensions = 20, res = 0.2) with the Louvain algorithm.

387

The identified clusters were then visualized using Uniform Manifold Approximation and Projection

388

(UMAP) embeddings (dimensions = 20).

389

Feature Plots were generated using Seurat function FeaturePlot using the RNA assay. The

390

module score was calculated from specified genes using the Seurat-implemented function

391

AddModuleScore and generated using the FeaturePlot function as described (Hao et al., 2021).

392

Module score is a calculate the average expression levels of each cluster at the single cell level

393

subtracted by the aggregated expression of random control feature sets (200 by default). Cluster

394

based differential expression was performed using the FindMarkers Seurat function using the RNA

395

assay or normalized RNA data. Volcano plots were generated using the EnhancedVolcano

396

[https://github.com/kevinblighe/EnhancedVolcano] R function based on normalized RNA assay data

397

from the NormalizeData function.

398

Averaged expression matrix from each cluster was exported from Seurat using the

399

AverageExpression function. Gene expression values were normalized using a row-based z-score

400

calculation function were plotted using pheatmap function (Gu et al., 2016).

401

Volcano plot: Raw RNA expression data were normalized using NormalizeData function with

402

default settings. This converts the expression values to natural log transformed using log1p.

403

Differential expression of all microglia clusters (0,1,2,3,4,6) was performed between conditions using

404

FindMarkers (only.pos = FALSE, min.pct = 0.15, logfc.threshold= 0.15). A total of 484 significant

405

JNeurosci Accepted Manuscript

genes

was

found.

Volcano

plots

were

generated

using

the

EnhancedVolcano

406

[https://github.com/kevinblighe/EnhancedVolcano].

407

Pathway enrichment analysis for microglia clusters was performed using Ingenuity Pathway

408

Analysis (IPA) (Kramer et al., 2014). Raw data for IPA analysis was found using Seurat

409

implemented function FindMarkers (only.pos = FALSE, min.pct = 0.15, logfc.threshold= 0.15) from

410

each cluster/condition tested against all other cells. Differential expression output was analyzed in

411

IPA core analysis using Expr log Ratio (confidence = experientially observed, species = Mouse,

412

Tissue & cells lines = Nervous system and all Macrophages, stringent filtering on molecules and

413

relationships). Infers likely activation states of biological functions based on comparison with a

414

model that assigns random regulation. IPA p-value was calculated with the Fischer's exact test,

415

reflects the likelihood that the association between a set of genes in dataset compared to IPA

416

datasets. Based on QIAGEN metrics, IPA pathway z-score of greater or equal to positive 2 is

417

considered activated, while a score less than or equal to negative 2 is considered an inhibited

418

pathway.Ratio is the number of genes from the list that maps to the pathway divided by the total

419

number of genes that map to the same pathway. Canonical Pathway and Upstream Regulator

420

figures were generated using ggplot2 dotplot function [https://ggplot2.tidyverse.org/].

421

Cell-cell communication analysis was preformed using CellChat (Jin et al., 2021) after clusters

422

were labeled. Standard preprocessing steps were preformed using the CellChatDB.mouse dataset.

423

Analysis was preformed independently between wildtype and knockout cells after removing non-

424

myeloid cells of interest and splitting the integrated dataset using Seurat SplitObject function. CellChat

425

wildtype and knockout objects were merged using merge CellChat function. Basic comparison

426

JNeurosci Accepted Manuscript

analysis was used to generate figures. Figures were generated using default settings for each

427

function.

428

429

Measurement and quantification of ECAR and OCR of rat and mouse microglia.

Extracellular

430

acidification rate (ECAR) and oxygen consumption rate (OCR) were measured using a Seahorse

431

XFe24 analyzer (RRID:SCR_019539, Agilent). Rat primary microglia were immuno-purified from the

432

mixed glia cultures isolated from P1-2 rats, and plated at 50,000-60,000 cells/well on an XF24 cell

433

culture microplate that was precoated with 0.1 mg/mL PDL, and cultured for 3-4 days. The cartridge

434

was hydrated in XF calibrant buffer overnight in a CO

-free 37

℃

incubator. On the day of the assay,

435

the assay medium for ECAR (XF base medium with 4 mM glutamine) and OCR (XF base medium

436

with 25 mM glucose, 1 mM pyruvate and 4 mM glutamine) were prepared freshly. To induce glycolytic

437

and respiratory stress, glucose (10 mM), 2-deoxy-D-glucose (2-

DG, 50 mM), oligomycin (1 μM),

438

FCCP (0.5 μM) and rotenone (0.5 μM) /antimycin A (0.5 μM) were prepared as 10X solutions in

439

respective assay media according to the Agilent’s instructions.

440

For ECAR and OCR measurement on adult mouse microglia, the control FAD:i-PKM2

and

441

experimental FAD:i-PKM2

MΔ

mice were treated daily with 75 mg/kg of tamoxifen dissolved in 10%

442

ethanol and 90% oil for 5 days via oral gavage at 1-month old. Two weeks after the last tamoxifen

443

dosing, mouse brains were isolated and dissociated using the Adult Brain Dissociation Kit (Miltenyi,

444

Cat#130-107-677)

according to the manufacturer’s instructions. Microglia were immuno-purified using

445

CD11b antibody-conjugated microbeads (Miltenyi, Cat#130-093-634), plated at 60,000 cells/well on

446

JNeurosci Accepted Manuscript

an XF24 cell culture microplate that was precoated with 0.1 mg/mL PDL, and cultured for 7 days in

447

DMEM supplemented with 10%FBS, 1%P/S, 50 ng/mL granulocyte-macrophage colony-stimulating

448

factor (GM-CSF), and 100 ng/mL macrophage colony-stimulating factor (M-CSF). One day before the

449

Seahorse experiment, the cartridge was hydrated in XF calibrant buffer overnight in a CO

-free 37

℃

450

incubator. The assay medium (XF base medium with 4 mM glutamine) was prepared freshly on the

451

day of the assay. Glucose (10 mM), 2-DG (50 mM), oligomycin

(1 μM), FCCP (1.5 μM) and rotenone

452

(0.5 μM) /antimycin A (0.5 μM) were prepared as 10X solutions in assay medium and added to the

453

cell cultures to induce glycolytic and respiratory stress during the Seahorse experiment.

454

455

Transmission electron microscopy.

Control FAD:i-PKM2

and experimental FAD:i-PKM2

MΔ

mice

456

were treated daily with 75 mg/kg of tamoxifen dissolved in 10% ethanol and 90% oil for 5 days via

457

oral gavage at 2-month old. At 4 months, mice were transcardially perfused with ice-cold saline. Brains

458

were isolated and dissociated using the Adult Brain Dissociation Kit (Miltenyi, Cat#130-107-677)

459

according to the manufacturer’s instructions. Microglia were immuno-purified using CD11b antibody-

460

conjugated microbeads (Miltenyi, Cat#130-093-634). Freshly isolated microglia were immediately

461

fixed in 2% paraformaldehyde/2.5% glutaraldehyde in 0.1 M sodium cacodylate buffer (pH 7.2) at

462

room temperature for 1 hr, and embedded in 2% agarose for TEM processing. Samples were post-

463

fixed with 1% osmium tetroxide and then en bloc stained with 1% aqueous uranyl acetate, dehydrated

464

in a graded series of ethanol, and embedded in Eponate 12 epoxy resin (Ted Pella Inc., Redding,

465

CA). Ultrathin sections were cut with a Leica EM UC6 ultramicrotome (Leica microsystems, EM FC7).

466

Images were acquired with an FEI Tecnai G2 Spirit BioTwin transmission electron microscope

467

JNeurosci Accepted Manuscript

(Thermo Fisher Scientific, Waltham, MA), and a Macrofire (Optronics, Inc., Chelmsford, MA) digital

468

camera and AMT image capture software. The length and area of mitochondrial cristae were

469

measured and quantified with ImageJ.

470

471

Quantitative PCR.

Total RNA was isolated from primary rat microglia and fresh mouse brain tissues

472

using TRIzol Reagent according to the user guide. Equal amounts of total RNA (0.5-

1 μg) were

473

reverse transcribed using the OneTaq RT-PCR kit (New England Biolabs, Cat#E5310). Quantitative

474

PCR (qPCR) was performed using PowerUp SYBR Green Master Mix (Applied Biosystems,

475

Cat#100029284) and a QuantStudio 3 Real-Time PCR Instrument (Applied Biosystems). The relative

476

mRNA levels of target genes were calculated using the ΔΔCt method, and Gapdh was selected as

477

the endogenous control.

478

479

Experimental design and statistical analysis.

Statistical analyses were performed using GraphPad

480

Prism v9.3.0 (RRID:SCR_002798). The data are expressed as mean ± SEM. The significance

481

between different experimental groups was tested by paired or

unpaired Student’s

-test as indicated

482

in the figure legends. When experimental groups include their own biological replicates, they are

483

indicated. The p-values that were less than 0.05 were presented in the figures.

484

485

RESULTS

486

JNeurosci Accepted Manuscript

Aberrant activation of PKM2 in

Alzheimer’s disease

487

As the first step to address a possible metabolic effect on AD pathology, we examined whether there

488

are differences in glycolytic enzyme expression between wild type control and 5XFAD mice. The

489

protein expression of most enzymes did not show any changes, except for hexokinase 2 (HK2), the

490

platelet isoform of phosphofructokinase (PFKP), and PKM2 (Fig. 1A, B, C, D). PKM2 in particular

491

was present mainly as active tetramers in the brain, and its levels were increased in 5XFAD mice

492

(Fig. 1C, D). Expression of its alternative isoform, PKM1, on the other hand showed no difference

493

between the control and 5XFAD mice. This is in agreement with a recent paper where they showed

494

an increase in PKM2 in 5XFAD mice, although they only tested for total PKM2 without distinguishing

495

the active tetramers from inactive monomers and dimers (Pan et al., 2022). Similar increases in

496

PKM2 tetramers and monomers but not PKM1 were detected when we analyzed human frontocortical

497

lysates from AD cases compared to age matched controls (Fig. 1E, F).

498

499

We next sought to determine which cell types in the brain express PKM2 by performing

500

immunohistochemistry on 5XFAD and control mouse brain sections at 4-5 months. In the wild type

501

mouse brain, PKM2 immunoreactivity was detectable at a very low level in Iba1

microglia and GFAP

502

astrocytes throughout different regions of the brain. In 5XFAD mice on the other hand, its expression

503

increased dramatically, especially among Iba1

microglia that were associated with Amylo-Glo-

504

labeled Aβ plaques (Fig. 2A, B). These PKM2

microglia near amyloid plaques also expressed high

505

levels of TREM2, with expression of PKM2 and TREM2 colocalizing completely (Fig. 2C, D). Since

506

JNeurosci Accepted Manuscript

TREM2 is known to regulate metabolic homeostasis in microglia (Ulland et al., 2017) and pyruvate

507

kinase (PK) is a key enzyme that determines glycolytic activity, the complete colocalization of PKM2

508

and TREM2 expression in plaque-associated microglia may suggest a critical role of metabolic

509

balance in TREM2-mediated function in microglia.

510

511

Microglial recruitment to amyloid plaques has been shown to be influenced by TREM2 (Wang et al.,

512

2015; Parhizkar et al., 2019) and

the microglia’s energy status (Ulland et al., 2017; Baik et al., 2019).

513

Since PKM2 is expressed in TREM2

microglia surrounding plaques, we asked whether activating

514

PKM2 among microglia would influence chemotactic migration of microglia to an injury site. To

515

address this question, PKM2 was activated with TEPP-46, a CNS penetrable PKM2-specific activator

516

(Anastasiou et al., 2012; Angiari et al., 2020; Seki et al., 2020) in Cx3cr1-GFP mice, in which migration

517

patterns of GFP

microglia toward injury site can be followed in vivo using 2-photon microscopy. Our

518

experimental scheme is depicted in Fig. 3A: After 2 weeks of recovery from surgery, mice were treated

519

once a day with TEPP-46 or vehicle for 4 days. 2-photon microscopy was conducted on the following

520

day. In vehicle-treated control mice, GFP

microglia were rapidly recruited to the injury site

521

reaching a peak in 45-60 min, while their migration was significantly slower and did not reach the peak

522

in 60 min in TEPP-46 treated mice (Fig. 3B, C). These results are somewhat similar to those of a

523

report where systemic delivery of 2-deoxyglucose (2-DG) completely blocked microglial migration for

524

the entire 60 min in the same Cx3cr1-GFP mice (Baik et al., 2019). The authors concluded that it

525

was mainly due to 2-DG inhibiting glycolysis. We thus tested whether our TEPP-46 treatment plan

526

was indeed effective in inducing tetramerization of PKM2 by isolating Cd11b

microglia and measuring

527

JNeurosci Accepted Manuscript

the extent of PKM2 tetramerization as in Fig. 1. A 4-day dosing of TEPP-46 was sufficient to induce

528

PKM2 tetramerization in microglia compared to the vehicle control (Fig. 3D), which we interpret as

529

suggesting that PKM2 must have influenced glycolysis and/or oxidative phosphorylation in TEPP-46

530

treated mice. It should also be noted that 2-DG is known to block both glycolysis and oxidative

531

phosphorylation (Wick et al., 1957; Wang et al., 2018).

532

533

Baik et al. has also shown that Cx3cr1-GFP

microglial motility was significantly reduced in 5XFAD

534

mouse background compared to the wild type when lesioned with laser (Baik et al., 2019). Since

535

TEPP-46 treatment increased tetramerization of PKM2 efficiently, and 5XFAD mice express high

536

levels of PKM2 tetramers in microglia at 6-8 months of age (Fig. 1C and 2C), it could be concluded

537

that the attenuated motility of Cx3cr1-GFP

microglia in 5XFAD mice was in part attributed to PKM2

538

tetramers.

539

540

To further investigate the effects of PKM2 activation on metabolism, we measured the effects of

541

TEPP-46 on microglia bioenergetics using Seahorse analyses of primary rat microglia with and

542

without TEPP-46. A 4-hr pre-treatment with TEPP-46 led to 20% reduction in basal glycolysis in

543

microglia, based on an ECAR assay (Fig. 3E, F). Maximum glycolytic capacity was also decreased

544

by 31%, which led to a 50% reduction in glycolytic reserve. In addition, 1-hr incubation of rat microglia

545

with TEPP-46 resulted in a 35% reduction in lactate production (Fig. 3G). In OCR assays, TEPP-46

546

had no effect on basal respiration, but reduced maximal respiratory capacity by 24% and spare

547

JNeurosci Accepted Manuscript

respiratory capacity by 29% (Fig. 3H, I). As a control, we demonstrate that 1-hr treatment with TEPP-

548

46 induced tetramerization of PKM2 (Fig. 3J). These results may sound opposite to what one expects

549

from TEPP-46 treatment based on the Warburg effect, but a growing literature suggests that the well-

550

accepted positive links between glycolysis with a proinflammatory response, and respiratory activity,

551

with an anti-inflammatory response may not be so strict. For instance, under conditions in which

552

PKM2 expression is induced, such as LPS treated macrophages, TEPP-46 treatment significantly

553

reduced IL-1



production (Palsson-McDermott et al., 2015). Similarly, deleting HK2, the first enzyme

554

in glycolysis, from Cx3cr1

microglia, resulted in reduction in both glycolytic and respiratory function

555

(Hu et al., 2022). We surmise that our data provide additional support for the emerging notion that

556

perturbations in the glycolytic pathway will have an impact not only on glycolysis but also on oxidative

557

phosphorylation under pathological conditions.

558

559

Selective deletion of Pkm2 in microglia leads to reduction in AD pathology in 5XFAD mice

560

To determine the role of

Pkm2

in microglial metabolism and its effect on AD pathology, we deleted

561

PKM2 from microglia by crossing 5XFAD mice with Cx3cr1-CreERT2:PKM2

fl/fl

mice. Tamoxifen-

562

mediated deletion of PKM2 from microglia was highly effective without causing a huge compensatory

563

increase in its isoform, PKM1, in the experimental FAD:i-PKM2

M∆

mice compared to the control FAD:i-

564

PKM2

mice (Fig. 4A, B). Selective deletion of PKM2 in microglia but not in astrocytes is also

565

confirmed by immunohistochemistry of 4-month-old mouse brain sections from both genotypes (Fig.

566

4C). In regard to its effect on overall AD pathology, deleting PKM2 in microglia resulted in a reduction

567

JNeurosci Accepted Manuscript

in AD pathology in 5XFAD mice: Both soluble and insoluble A



42 levels were reduced in FAD:i-

568

PKM2

M∆

mice compared to the control via Western blots and ELISA assays at 4-5 months (Fig. 4D,

569

E). This reduction was reflected in a decline in amyloid plaque numbers and in their sizes upon

570

deleting PKM2; the proportion of plaques that were less than 25



was significantly higher in FAD:i-

571

PKM2

M∆

mice compared to its control, while the proportion of plaques greater than 250



was

572

significantly reduced in FAD:i-PKM2

M∆

mice than in the control (Fig. 4F, G, H). In line with these data,

573

the number of active synapses was increased in the stratum oriens of the CA1 region of the

574

hippocampus (Fig. 4I, J). Active synapses were defined by synaptic puncta which showed apposing

575

or overlapping PSD95 and vGlut1 signals. These results together suggest that aberrant activation of

576

PKM2 in microglia contributes significantly to AD pathology. This phenotype of FAD:i-PKM2

M∆

mice

577

is almost identical to what was published in Pan et al. (Pan et al., 2022).

578

579

Selective deletion of Pkm2 from microglia in 5XFAD mice leads to activation of DAM phenotype

580

in microglia

581

Under neurodegenerative conditions, microglia undergo transcriptomic changes that are

582

characterized as homeostatic, DAM1, and DAM2 stages (Keren-Shaul et al., 2017). While TREM2

583

activation is crucial for developing the DAM2 signature, the signal that induces the change from

584

homeostasis to the DAM1 stage is not clear. Since retaining TREM2, a gene belonging to the DAM2

585

stage, is protective against AD pathology (Keren-Shaul et al., 2017; Zhou et al., 2020), an increase

586

in DAM gene expression would be regarded as being beneficial in general, bestowing microglia with

587

JNeurosci Accepted Manuscript

the correct cellular tools to remove toxic amyloid. We hypothesized that metabolic stress is a signal

588

that regulates the development of the DAM1 signature in microglia. To address whether PKM2

589

deletion altered DAM signatures in microglia, and whether the analyses would unveil subclusters of

590

microglia that are critical for glucose metabolism, we performed snRNAseq analyses of microglia

591

isolated with Cd11b antibody-conjugated beads from FAD:i-PKM2

M∆

and control FAD:i-PKM2

mice

592

at 4 months of age using the 10X Genomics Chromium Next GEM Single Cell 3’ Reagent kits

593

(Extended Table 5-1). From 3 mice per genotype, a total of 15,636 and 18,512 cells from FAD:i-

594

PKM2

and FAD:i-PKM2

M∆

microglia, respectively, were selected for subsequent analyses after

595

QC/filtering steps. Following unbiased cell clustering, 13 distinct cell clusters were identified, which

596

is also shown as Uniform Manifold Approximation and Projection (UMAP) (Fig. 5A). Of the 13 clusters,

597

6 (namely clusters 0, 1, 2, 3, 4, and 6) were microglial clusters comprising 83.5% and 87.0% of the

598

total from the control and FAD:i-PKM2

M∆

mice, respectively (Fig. 5B). A macrophage cluster was also

599

identified as cluster 5, which comprised 9.16% and 7.64% of the total from the control and FAD:i-

600

PKM2

M∆

mice, respectively, reflecting the microglia/macrophage-specific Cx3cr1-CreERT2 line used

601

for the study. The rest of the clusters comprised less than 2% of the total, so they were not included

602

in subsequent analyses. All 6 microglia plus macrophage clusters expressed the

Pkm

gene, and its

603

expression was lowest in cluster 0, while its expression was significantly higher in clusters 4 and 6

604

(Fig. 5C and Extended Table 5-2). It should be clarified that upon Cre-mediated recombination, only

605

Pkm2

-specific exon 10 becomes deleted while leaving the rest of the

Pkm

gene intact, allowing

606

detection of the

Pkm

gene by snRNAseq (Israelsen et al., 2013). When the 484 DEG were analyzed

607

in a Volcano plot (Extended Table 5-3), we found many DAM genes, such as

ApoE

, were among

608

JNeurosci Accepted Manuscript

those whose levels increased most upon

Pkm2

deletion, which is highlighted with pink arrows in the

609

Volcano plot (Fig. 5D). Individual UMAP feature plots of

ApoE

expression additionally illustrate that

610

Pkm2

deletion resulted in a substantial increase in

ApoE

expression in clusters 4 and 6 in particular,

611

although its expression was increased in all microglial clusters (Fig. 5E).

Trem2

expression was also

612

increased in cluster 4 in FAD:i-PKM2

M∆

mice, although the extent of its increase is much less than

613

that of

ApoE

, probably due to the age at the time of the microglia isolation for snRNAseq, 4 months.

614

ApoE

expression was shown to precede the expression of

Trem2

in 5XFAD mice (Keren-Shaul et al.,

615

2017). In accordance with the snRNAseq data, we observed ~2-fold increase in APOE but not TREM2

616

protein expression in Cd11b

microglia isolated from 4-5 month-old FAD:i-PKM2

M∆

mice (Fig. 5F, G).

617

618

Changes in all the DAM genes in each of the microglia and macrophage clusters were examined as

619

heatmap analyses. A heatmap plot of DAM genes revealed that ~75% of them were expressed highly

620

in clusters 4 and 6, with cluster 6 expressing ~50% of DAM genes (Fig. 6A). With

Pkm2

deletion,

621

38% of homeostatic genes were downregulated in the cluster 6, while 63% of homeostatic genes in

622

the cluster 6 were upregulated. Among DAM stage 1 and 2 genes in clusters 2, 4, and 6, 81% showed

623

increases

while only 17% showed a decrease upon

Pkm2

deletion (Fig. 6B). The majority of genes

624

whose expression increased with

Pkm2

deletion belongs to clusters 4 and 6, which express a higher

625

level of

Pkm

mRNA compared to other microglial clusters, suggesting glucose imbalance, perhaps

626

mediated by the increase in the

Pkm

gene in 5XFAD mice, influences DAM gene expression. A

627

similar pattern of gene expression was observed when we analyzed AD risk genes (Jansen et al.,

628

2019; Kunkle et al., 2019; Schwartzentruber et al., 2021; Wightman et al., 2021; Bellenguez et al.,

629

JNeurosci Accepted Manuscript

2022): ~65% of AD risk genes were highly expressed in cluster 6 (Fig. 6C), and

Pkm2

deletion

630

resulted in both up and downregulation of genes (Fig. 6D). This is in agreement with a recent report

631

that demonstrated that the ‘sugar metabolism’ module is also enriched with proteins that are

632

implicated as AD genetic risk factors (Johnson et al., 2020).

633

634

We also wanted to compare changes in homeostatic and DAM genes among clusters 2, 4, and 6 from

635

each other in FAD:i-PKM2

and FAD:i-PKM2

M∆

mice. Changes included both increased and

636

decreased genes upon

Pkm2

deletion. The proportion of each class of genes out of the 56 total that

637

expressed homeostatic, DAM1, and DAM2 genes is presented for clusters of 2, 4, and 6 as pie

638

diagrams (Fig. 6E).

Between the two genotypes, the proportions in each cluster didn’t change greatly

639

in each class of homeostatic, DAM1, and DAM2 genes. When we distinguished those that increased,

640

decreased, and were unchanged upon

Pkm2

deletion, however, the differences became obvious (Fig.

641

6F). In cluster 2, a majority of genes did not change, except for some increase in DAM2 genes. In

642

cluster 4, homeostatic genes remained unchanged, while both DAM1 and DAM2 genes increased

643

dramatically. Also in cluster 6, large changes were observed, especially with homeostatic genes, all

644

of which showed either an increase or decrease. Like cluster 4, DAM1 genes in cluster 6 increased

645

greatly. A majority of DAM2 genes on the other hand remained unchanged, although some showed

646

an increase. These results highlight our notion that

Pkm2

deletion resulted in a significant increase

647

in DAM1 genes, especially in clusters 4 and 6. Quantitative PCR analyses of selective AD risk and

648

DAM genes using RNA from isolated microglia confirmed the snRNAseq data (Fig. 6G). Based on

649

these data, we surmised clusters 4 and 6 are good candidates for metabolic microglia clusters.

650

JNeurosci Accepted Manuscript

651

Cluster 6 is metabolic microglia cluster

652

Ingenuity pathway analyses using 15 top canonical cellular pathways revealed that cluster 6 differs

653

from the other microglial clusters: When comparing the plots for cluster 6 to clusters 0 to 5, for each

654

criterion of biological function, cluster 6 exhibits a generally reversed relationship for each Z-score

655

sign and magnitude. In more detail, the genes whose levels increased in clusters 0 to 5 include those

656

that play roles in RhoGDI signaling, PPAR signaling, PTEN signaling, VDR/RXR activation,

657

PPAR



/RXR



activation, and cell cycle regulation. The genes whose levels increased in cluster 6 on

658

the other hand included those for adherens junction, CDC42 signaling, spliceosomal cycle, AMPK

659

signaling, autophagy, RAC signaling, PLC signaling, and the senescence pathway (Fig. 7A).

Pkm2

660

deletion did not alter these distinct features of cluster 6 greatly, except for the strength of each

661

pathway. As a way to validate the significance of the Ingenuity pathway analyses, we analyzed

662

whether the extent of AMPK activation, a major metabolic regulator of global cellular energy

663

homeostasis, was altered in microglia by performing Western blotting of proteins extracted from

664

Cd11b

microglia from FAD:i-PKM2

and FAD:i-PKM2

M∆

mice. P-AMPK



and p-ACC levels were

665

elevated significantly in FAD:i-PKM2

M∆

compared to those in FAD:i-PKM2

mice (Fig. 7B, C),

666

suggesting

Pkm2

deletion had an effect on AMPK activation. Although this data do

esn’t derive

667

exclusively from cluster 6 microglia per se, it should be regarded as included. We thus conclude that

668

cluster 6 microglia are unique in their signaling capacity, including the AMPK pathway. This data

669

corresponds well with the report that PKM knockdown increased AMPK



phosphorylation in cancer

670

cell lines (Prakasam et al., 2017).

671

JNeurosci Accepted Manuscript

672

Cluster 6 also stood out when we performed CellChat analysis with differentially expressed genes

673

from microglial/macrophage clusters (Jin et al., 2021): CellChat analysis identified 12 shared ligand-

674

receptor interactions with high significance (Fig. 8A). From the CellChat analyses, we focused on Axl

675

signaling via ProS/Gas6, since not only

Axl

expression increased dramatically in cluster 6 in FAD:i-

676

PKM2

M∆

compared to that in FAD:i-PKM2

mice (Fig. 8B, C), but also its signaling capacity increased

677

dramatically in cluster 6 in FAD:i-PKM2

M∆

compared to that in FAD:i-PKM2

mice (Fig. 8D). Axl is a

678

member of the Tyro3-Axl-Mer (TAM) receptor family, involved in engulfing amyloid plaques (Huang

679

et al., 2021). We hypothesized that Axl expression and signaling may represent a cluster 6-mediated

680

event. Indeed, Axl expression colocalized with PKM2 among microglia that were near a plaque, and

681

not with microglia that were not associated with plaques (Fig. 8E). In addition, we observed an

682

increase in p-Axl/Axl signals from the extracts isolated from microglia from FAD:i-PKM2

M∆

compared

683

to FAD:i-PKM2

mice (Fig. 8F, G). Although we could not tell if Axl activation occurs in cluster 6 at

684

this time, these results together support the notion that cluster 6 differs from other

685

microglial/macrophage clusters. We thus interpret these data as suggesting that microglia belonging

686

to cluster 6 are among those that are actively recruited to amyloid plaques, where they phagocytose

687

amyloid



, and efferocytose damaged cells. In accordance with the data, ~64% of phagocytic genes

688

were expressed in cluster 6 (Fig. 8H).

689

690

Cluster 6 contains genes involved in glycolysis and TCA cycle.

691

JNeurosci Accepted Manuscript

In support of our hypothesis that cluster 6 is a metabolic cluster, we found that a majority of genes in

692

the glycolytic pathway and in TCA cycles are enriched in cluster 6 (Fig. 9A, C). Upon deleting

Pkm2

693

expression of more than 50% of the genes increased further in cluster 6 (Fig. 9B, D), suggesting a

694

crucial role of PKM2 in metabolic environment in cluster 6 microglia. When we analyzed glycolytic

695

enzyme expression in Cd11b

microglial extracts from FAD:i-PKM2

and FAD:i-PKM2

M∆

mice, the

696

increase upon

Pkm2

deletion was reflected only among HK1, LDHa, and PDH, but not in HK2 and

697

GAPDH, although their protein expression levels were increased in FAD:i-PKM2

M∆

mice (Fig. 9E, F).

698

Among enzymes from the TCA cycle, malate dehydrogenase 2 (MDH2) protein levels were increased

699

in FAD:i-PKM2

M∆

mice compared to its control, while citrate synthase (CS) protein levels did not show

700

a statistically significant difference (Fig. 9G, H). We interpret these results as suggesting that PKM2

701

deletion influences not only glycolytic but also TCA cycle enzyme levels in microglia.

702

703

PKM2 deletion leads to restoration of both glycolytic and respiratory capacity in adult

704

microglia from 5XFAD mice

705

We decided to perform functional assays to measure changes in glycolysis and mitochondrial

706

respiration with

Pkm2

deletion by employing Seahorse Analyzer. For this, we immunopurified Cd11b

707

microglia at 6 weeks from both genotypes and measured both ECAR and OCR. Microglia from FAD:i-

708

PKM2

M∆

mice exhibited a significant increase in both basal and maximal glycolytic capacity in ECAR

709

assays; basal glycolysis increased by 79% and maximal glycolytic capacity by 2-fold (Fig. 10A, B).

710

Glycolytic reserve was increased but not significantly. These results are opposite to rat primary

711

JNeurosci Accepted Manuscript

microglia treated with a PKM2 metabolic activator, TEPP-46, where basal glycolysis, glycolytic

712

capacity, and glycolytic reserve were reduced (Fig. 3E, F, G). These results suggest that metabolic

713

activation of PKM2 is inhibiting overall microglial glycolytic capacity without affecting glycolytic

714

reserve. Regarding mitochondrial respiration,

Pkm2

deletion did not have any effect on basal

715

respiration, but increased maximal respiration by 31%, which resulted in a 50% increase in spare

716

respiratory capacity (Fig. 10C, D). These results are again opposite to those from rat microglia treated

717

with TEPP-46, where basal respiration was unaffected, while the maximal respiration and respiratory

718

reserve were reduced (Fig. 3H, I). These results suggest that metabolic activation of PKM2 has a

719

negative effect on respiration as well as on glycolysis. In particular, the 50% increase in respiratory

720

reserve but not glycolytic reserve observed with

Pkm2

deletion is surprising, since it suggests that

721

when there is an emergency metabolic demand for ATP, microglia are likely to utilize mitochondrial

722

respiration rather than glycolysis (Marchetti et al., 2020). We caution, however, that we cannot rule

723

out the possibility that the bioenergetics differ based on the species difference, namely between

724

primary rat and mouse cultures.

725

726

We next asked whether microglial mitochondria exhibited any structural changes in 5XFAD mice and

727

whether

Pkm2

deletion had any effect on them. To address this question, we performed transmission

728

electron microscopy (TEM) analyses of freshly immunopurified and immediately fixed Cd11b

bead-

729

isolated microglia from both genotypes. We found that the length, width and total area of mitochondria

730

were not very different between the two genotypes, but cristae appeared bulged in FAD:i-PKM2

731

mitochondria rather than the normal thin doublets in FAD:i-PKM2

M∆

mitochondria (Fig. 10E).

732

JNeurosci Accepted Manuscript

Quantification of the cristae revealed the average cristae area in each mitochondrion was significantly

733

reduced as well as their length in FAD:i-PKM2

M∆

mitochondria, although the length reduction was not

734

statistically significant (Fig. 10F, G). Since mitochondrial respiration is intimately linked to cristae,

735

these results demonstrate that 5XFAD mice exhibit a defect in mitochondrial respiration in part by

736

metabolic activation of PKM2, as the defect in respiration in the microglia of 5XFAD mice becomes

737

somewhat corrected by deleting

Pkm2

. A mitochondrial respiratory defect has been reported in AD

738

mice (Yao et al., 2009), although these data were from total brain mitochondria and not just microglia.

739

740

In situ spatial metabolomics analyses of plaque-bearing microglia in 5XFAD mice suggest

741

increased respiratory activity upon Pkm2 deletion

742

We decided to address the contribution of glycolysis and/or mitochondrial respiration on microglial

743

function by directly measuring microglial metabolites in situ, employing matrix-assisted laser

744

desorption/ionization mass spectrometry imaging (MALDI-MSI).

Brain metabolism does not differ

745

wildly in living animals, and MALDI is capable of identifying subtle sample by sample differences over

746

thousands of datapoints from a brain.

In addition, the approach obviates the need to isolate microglia

747

during which process small metabolites can be lost

With combined MALDI-MSI and

748

immunohistochemistry, we analyzed the abundance of a majority of glycolytic and TCA cycle

749

metabolites among plaque-bearing or plaque-associated microglia in the cortex, hippocampus, and

750

subiculum of FAD:i-PKM2

and FAD:i-PKM2

M∆

mice. The pyruvate data is shown as an example in

751

Fig. 11A, with a high-magnification view used for quantification after overlaying both Iba1 IHC results

752

JNeurosci Accepted Manuscript

and AmyloGlo plaque staining (Fig. 11B). We quantified the abundance of pyruvate in Iba1

microglia

753

that bear or are close to AmyloGlo

plaques, and the results demonstrated a significant reduction in

754

FAD:i-PKM2

M∆

mice (Fig. 11C), which was as expected, since pyruvate is the direct product of PKM2.

755

Also shown are all the metabolites that were measured in Fig. 11D, E, and F. A summary of glycolytic

756

and TCA cycle metabolites data is shown in Fig. 12A. While pyruvate and glyceraldehyde 3-

757

phosphate levels were altered in FAD:i-PKM2

M∆

microglia, most glycolytic metabolites did not show

758

great changes. On the other hand, citrate, cis-aconitate, and malate levels were decreased in FAD:i-

759

PKM2

M∆

microglia in addition to derivatives of oxaloacetate, such as aspartate and N-acetylaspartate

760

as well as glutamate and glutamine that are derived from



-ketoglutarate (Fig. 12A). These results

761

are in agreement with the 50% increase observed in respiratory but not glycolytic reserve with

Pkm2

762

deletion in microglia (Fig. 10).

763

764

Our hypothesis is that metabolic stress is one of the key signals that inhibit homeostatic microglia

765

from transitioning into the DAM1 stage. Since our results suggest that aberrant PKM2 activation

766

disrupts not only glycolytic capacity but also depletes respiratory reserve that can be drawn from when

767

ATP demand is high in microglia (Marchetti et al., 2020), we asked whether inducing glycolytic and/or

768

respiratory metabolic stress would change mRNA levels of

ApoE

and

Trem2

in rat primary microglial

769

cultures. Modulating respiratory capacity alone with IACS-010759, which inhibits the complex I of the

770

electron transport chain or dichloroacetate (DCA), which in turn facilitates oxidative phosphorylation

771

at the expense of glycolysis by disinhibiting pyruvate dehydrogenase (Bonnet et al., 2007) did not

772

have any effect (Fig. 12B, C). TEPP-46 and 2-DG, both of which have an impact on glycolysis and

773

JNeurosci Accepted Manuscript

oxidative phosphorylation, however, reduced both

ApoE

and

Trem2

mRNA levels by 32-36% and 40-

774

52%, respectively. We interpret these results as suggesting that both proper glycolytic and respiratory

775

inputs are necessary to induce DAM1 stage genes in microglia.

776

777

778

DISCUSSION

779

Although metabolic dyshomeostasis in the brain has been recognized as an important factor in AD

780

pathology, it has not been clear whether this was due to a brain-wide defect in glucose metabolism

781

or a dysfunction in a particular cell type. Here we report that reversing the metabolic defect in one

782

group of cells, microglia, led to a protective effect against AD pathology in 5XFAD mice. Although

783

our results do not rule out contributions from other cell types, whose own metabolic dysfunction could

784

influence overall AD pathology, the reduction in AD pathology observed in 5XFAD mice with microglial

785

Pkm2

deletion suggests that at a minimum, the metabolic health of microglia themselves is critically

786

important. Along with recent reports that microglia are the major cell types that take up glucose, and

787

the microglial activation state correlates with FDG uptake in human cases (Xiang et al., 2021), our

788

report highlights the importance of maintaining the metabolic heath of microglia in overall AD

789

pathology.

790

791

Here, our snRNAseq analyses have identified 6 microglial clusters, clusters 0, 1, 2, 3, 4, and 6. This

792

is twice the number of clusters that were initially identified in 5XFAD mice (Keren-Shaul et al., 2017).

793

JNeurosci Accepted Manuscript

The difference is likely due to a larger number of microglia that were sequenced in our study, 15-

794

18,000, compared to 8,000 cells sequenced in Keren-Shaul et al. (Keren-Shaul et al., 2017). In

795

addition, of their 8,000 cells, ~6,000 were microglia, while 13-16,000 were microglia in our study.

796

Perhaps due to an increase in the number of microglial cells sequenced, we have identified the distinct

797

cluster 6 in our study. Not only are about half of all DAM genes and AD risk genes expressed in

798

cluster 6, but also expression is higher in cluster 6 of genes for glycolysis and TCA cycle, perhaps

799

behaving as a ‘metabolic cluster’. Enrichment of metabolic genes in cluster 6 may suggest that it is

800

metabolically more active than other microglial clusters. An increase in AMPK signaling in cluster 6

801

from Ingenuity Pathway analyses as well as actual detection of increased p-AMPK



and p-ACC levels

802

in isolated microglia may be regarded as supporting this contention.

803

804

We found that microglia in 5XFAD mice have mitochondrial alterations with enlarged cristae structure,

805

and

Pkm2

deletion at least partially corrected it. Mitochondria are known to function as a signaling

806

organelle in addition to being responsible for bioenergetics, releasing metabolites of the TCA cycle

807

(Weinberg et al., 2015). These metabolic intermediates also function as signaling molecules

808

influencing cell phenotypes especially in immune cells, such as succinate, which has been implicated

809

in pro-inflammatory macrophage responses (Mills et al., 2016). A prevailing view was that a

‘glycolytic

810

switch’ was necessary for the inflammatory state in immune cells, while oxidative phosphorylation and

811

fatty acid oxidation accompanied protective anti-inflammatory differentiation (Vats et al., 2006;

812

Tannahill et al., 2013; Huang et al., 2016; Mills et al., 2016). These studies, however, relied on

813

isolated immune cells rather than investigating the bioenergetics in intact tissues. Even with

814

JNeurosci Accepted Manuscript

macrophages in tissue preparations, isolated from individual organs rather than collecting circulating

815

macrophages, bioenergetic analyses unveiled stark differences among macrophages in different

816

organs (Wculek et al., 2023): the authors perturbed oxidative phosphorylation by deleting

Tfam

in an

817

organ-specific manner, and discovered that oxidative phosphorylation rather than glycolysis was

818

central in macrophage differentiation. This demonstrates that immune cells are metabolically plastic,

819

adjusting their metabolic responses depending on need in their immediate microenvironment. In our

820

study, we sought to avoid the need to isolate microglia at all by employing in situ spatial metabolomics

821

on intact mouse brain sections. Our data illustrated that

Pkm2

deletion in microglia of 5XFAD mice

822

exhibited reduction in TCA intermediates, e.g., an increased TCA cycle activity (Buescher et al., 2015;

823

Niedenfuhr et al., 2015), while changes in the glycolytic metabolites were relatively minor. In addition,

824

we found that there were significant changes in some of the fatty acids, such as linoleic acid, oleic

825

acid, stearic acid, arachidonic acid as well as ethanolamines (Fig. 11F), which we interpret as

826

suggesting increased demand of fatty acid oxidation in FAD:i-PKM2

M∆

microglia as it is typically

827

associated with increased oxidative phosphorylation (Weinberg et al., 2015).

828

829

We found that HK2 protein levels increased in 5XFAD mice compared to wild type controls and also

830

with

Pkm2

deletion, although the increase was not statistically significant in the latter case.

831

Additionally, its mRNA expression is enriched in cluster 2 in FAD:i-PKM2

microglia, but becomes

832

enriched in cluster 6 in FAD:i-PKM2

M∆

microglia (Fig. 9A, B). HK2 is a key enzyme in glycolysis

833

converting glucose to glucose-6-phosphate. It has been shown recently that HK2 is also expressed

834

highly in microglia under neuropathological conditions including AD (Hu et al., 2022). When HK2

835

JNeurosci Accepted Manuscript

was deleted with the same Cx3cr1-CreERT2 as we have used here, they observed a decrease in

836

both glycolytic and respiratory capacity. This is similar to our data with

Pkm2

deletion, which resulted

837

in influencing both glycolysis and respiration (Fig. 10), albeit the fact that the effect of

Hk2

and

Pkm2

838

deletion on glycolysis and respiration are opposite to one another, possibly due to the fact that PKM2

839

can function as a metabolic regulator as a tetramer as well as a transcription factor in the

840

monomer/dimer form. Nonetheless, these results suggest that perturbations in the glycolytic pathway

841

will have an impact on not only glycolysis but also on oxidative phosphorylation.

842

843

Very recently, Pan et al. published that microglial deletion of

Pkm2

in 5XFAD mice resulted in a

844

reduction in amyloid pathology (Pan et al., 2022), as we are reporting here. Our data are mutually

845

supportive, in that our phenotypes on AD pathology are very similar. In the study by Pan et al., the

846

phenotype was attributed to a reduction in lactate production, which led to a reduction in lactyl

847

modification in histone H4K12 and its transcriptional targets of inflammatory cytokines upon deleting

848

Pkm2

in microglia. They, however, observed a ~3 fold compensatory increase in PKM1 protein levels

849

in microglia upon deleting PKM2, which we did not. The reason for the difference is not clear, but it

850

may be due to the amount of tamoxifen administered; Pan et al. used 4 mg, while we used 1.5 mg of

851

tamoxifen per mouse. Regardless of the reason, the question of interest is how much the observed

852

increase in PKM1 contributed to overall lactate levels, since PKM1 is constitutively active with a

853

greater activity than PKM2 (Christofk et al., 2008b; Christofk et al., 2008a).

854

855

JNeurosci Accepted Manuscript

In the nervous system, PKM2 expression was shown to increase under various pathological

856

conditions in areas such as the retina, Schwann cells, and also in transcriptionally induced neurons

857

(iNs) from iPSC (Zhang et al., 2015; Rajala et al., 2018; Babetto et al., 2020; Seki et al., 2020; Han

858

et al., 2021; Traxler et al., 2022). These findings suggest that metabolic imbalance accompanies

859

various neuropathological conditions and PKM2 and possibly HK2 (Hu et al., 2022) are likely to play

860

important roles in shaping the outcome. Traxler et al. in particular illustrated that the effect of PKM2

861

was responsible for iNs to adopt aerobic glycolysis in culture, and attributed the metabolic effect to

862

nuclear PKM2 (Traxler et al., 2022). It is highly likely that when PKM2 expression is dramatically

863

upregulated, a portion of PKM2 translocates to the nucleus, although a majority of PKM2 appeared

864

in the cytoplasm and processes as we have observed in microglia. Despite such a large increase in

865

PKM2, probably as tetramers in microglial cytoplasm, it is plausible that the observed effect includes

866

nuclear activation of PKM2. What regulates nuclear translocation of PKM2 is likely to depend on the

867

affected cell types under neuropathological conditions.

868

869

We acknowledge that the findings from our study of PKM2 in microglia from 5XFAD mice are unlikely

870

to be analogous to those of PKM2 in microglia from non-FAD background. Our results attest

871

nonetheless to their own significance, since PKM2 exerts its impact on metabolism after its levels

872

increase dramatically under pathological conditions. For instance, we have shown that deleting

Pkm2

873

in CD4

T cells had hardly any effect until the mice were subjected to EAE induction (Seki et al.,

874

2020). Phenotypes from PKM2 activation with TEPP-46 was also observed only after perturbations

875

of T cell differentiation or demyelination injuries. These results are consistent with the fact that

876

JNeurosci Accepted Manuscript

microglia undergo polarization only when they encounter external stimuli of both pro- and anti-

877

inflammatory cytokines (Madore et al., 2020). In our study, the FAD phenotype was the stress stimuli.

878

879

Johnson et al.

reported that PKM protein levels increase in the ‘sugar metabolism’ module from AD

880

cases compared to control subjects (Johnson et al., 2020), although they did not make the distinction

881

between PKM1 and PKM2. The

‘sugar metabolism’ module was enriched with astrocytes and

882

microglia, supporting our results. Surprisingly, the authors found that PKM protein is released into

883

CSF along with LDHb, and the amount released correlated with disease progression, and with a

884

decrease of amyloid



and an increase of tau/p-tau ratios in the CSF. This correlation may suggest

885

that glucose homeostasis is also likely to contribute to tau pathology in human AD. In addition, since

886

its increase in CSF is detected in asymptomatic cases, it may be possible to develop an assay for

887

PKM2 as an early diagnostic for incipient AD.

888

889

890

JNeurosci Accepted Manuscript

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JNeurosci Accepted Manuscript

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JNeurosci Accepted Manuscript

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1078

1079

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JNeurosci Accepted Manuscript

FIGURE LEGENDS

1081

Figure 1. Aberrant activation of PKM2 in Alzheimer’s disease.

(A)

Changes in glycolytic gene

1082

expression in the brains of 5XFAD mice. HK1, hexokinase 1; HK2, hexokinase 2; PFKP,

1083

phosphofructokinase, platelet; PFKFB3, 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3;

1084

GAPDH, glyceraldehyde-3-phosphate dehydrogenase; LDHA, lactate dehydrogenase a; PDH,

1085

pyruvate dehydrogenase; PDHK1, pyruvate dehydrogenase

kinase 1.

(B)

Quantification of the blots

1086

in (A) (n=5, all M mice for each genotype).

(C)

PKM2 Western blot of protein lysates obtained from 6-

1087

month-old 5XFAD mice following DSS crosslinking. Control blots of PKM1 and ERK are also shown

1088

along with total PKM2 without crosslinking.

(D)

Quantification of total, active tetrameric, and inactive

1089

monomeric PKM2 in (C) (5F mice for each genotype).

(E)

PKM2 Western blot of protein lysates

1090

obtained from frontal cortices of human AD and control subjects following DSS crosslinking. Control

1091

blots of PKM1 and ERK are also shown along with total PKM2 without crosslinking.

(F)

Quantification

1092

of total, active tetrameric, and inactive monomeric PKM2 in (E). (n=7, 4M + 3F, control and n=21,

1093

10M + 11F, human AD subjects).

Data are expressed as mean ± SEM. Unpaired Student’s

-tests

1094

were used in (B, D and F).

1095

1096

Figure 2. Upregulation of PKM2 in microglia in 5XFAD mice.

(A)

Representative images showing

1097

PKM2 expression in wild type and 5XFAD mice at 4-5 months. PKM2 expression is selectively

1098

increased among Iba1

microglia that associate with plaques, and not in GFAP

astrocytes in 5XFAD

1099

mice. Note that pink arrows mark GFAP

, while white arrows mark Iba1

cells. (n=5, 2M + 3F, for WT

1100

JNeurosci Accepted Manuscript

and n=5, 3M + 2F, for 5XFAD mice)

(B)

Imaris 3D reconstruction images showing colocalization of

1101

PKM2 with Iba1

microglia but not GFAP

astrocytes in 5XFAD mice.

(C)

Representative images

1102

showing PKM2 expression colocalizes completely with TREM2 expression near the plaque in 5XFAD

1103

mice at 6-7 months. White arrows indicate cells that are positive for both PKM2 and TREM2. (n=3,

1104

2M + 1F)

(D)

Imaris 3D reconstruction images showing colocalization of TREM2 with PKM2 and Iba1.

1105

1106

Figure 3. Pharmacological PKM2 activation impaired microglial recruitment as well as

1107

glycolysis and oxidative phosphorylation. (A)

A diagram of experimental time line for 2-photon

1108

imaging study in B, C, and D.

(B)

Representative images of in vivo 2-photon imaging of Cx3cr1-GFP

1109

microglia after laser-induced injury with and without TEPP-46 at 50 mg/Kg daily for 4 days. Smaller

1110

circles depict the laser-injury epicenter, while larger images represent the region of interest (ROI),

1111

from which quantification of microglial recruitment was calculated for 1 hr.

(C)

Quantification of GFP

1112

signals in the ROI in (B) (n=4, all M, for each treatment group).

(D)

Lysates from isolated microglia

1113

from the vehicle or TEPP-46 treated mice were subjected to DSS crosslinking and followed by PKM2

1114

Western blotting (n=3, 2M + 1F, for vehicle and n=3, 2M + 1F, for TEPP-46 group).

(E)

The effect of

1115

TEPP-46 on extracellular acidification rate (ECAR) in primary rat microglial cultures. Rat primary

1116

microglia were incubated for 4 hrs with of 75



M TEPP-46 prior to ECAR and OCR measurements.

1117

(F)

Quantification of the changes in glycolysis, glycolytic capacity, and glycolytic reserve by TEPP-46

1118

in (E) (n=3-5 replicates for each treatment with n=4 independent experiments).

(G)

Changes in lactate

1119

concentration in the media from rat microglial cultures treated for 1 hr with vehicle and TEPP-46 (n=3

1120

replicates for each treatment).

(H)

The effect of TEPP-46 on oxygen consumption rate (OCR) in

1121

JNeurosci Accepted Manuscript

primary rat microglial cultures.

(I)

Quantification of the changes in basal and maximal respiration, and

1122

spare respiratory capacity by TEPP-46 in (H) (n=3-5 replicates for each treatment with n=5

1123

independent experiments).

Data are expressed as mean ± SEM. Paired Student’s

-test was used in

1124

(C)

, and unpaired Student’s

-test was used in (F, G, I).

(J)

TEPP-46 induced PKM2 tetramers in rat

1125

microglia at concentrations ranging from 10 to 100



1126

1127

Figure 4. Selective deletion of Pkm2

in microglia led to reduction in Alzheimer’s pathology in

1128

5XFAD mice. (A)

Western blotting of lysates from microglia isolated from FAD:i-PKM2

and FAD:i-

1129

PKM2

M∆

mice at 4-5 months of age.

(B)

Quantification of PKM1 and PKM2 levels in (A) (n=3, 2M+1F,

1130

for FAD:i-PKM2

and n=3, 1M+2F, for FAD:i-PKM2

M∆

mice).

(C)

Representative images showing

1131

PKM2 expression lost selectively in microglia (white arrows) and not in astrocytes (pink arrows). (n=3,

1132

3M, for FAD:i-PKM2

and n=3, 2M+1F, for FAD:i-PKM2

M∆

mice).

(D)

Western blotting of both soluble

1133

and insoluble

amyloid β peptides from FAD:i-PKM2

and FAD:i-PKM2

M∆

mice at 4-5 months of age.

1134

(E)

ELISA analysis of both soluble and insoluble A



42 levels from FAD:i-PKM2

and FAD:i-PKM2

M∆

1135

mice at 4-5 months of age (n=8, 4M + 4F, for FAD:i-PKM2

and n=7, 2M + 5F, for FAD:i-PKM2

MΔ

1136

mice).

(F)

Representative images of plaques

stained for Aβ peptides in the cortex of FAD:i-PKM2

1137

and FAD:i-PKM2

M∆

mice at 4-5 months of age.

(G)

Quantification of the relative number of plaques in

1138

FAD:i-PKM2

and FAD:i-PKM2

M∆

mice at 4-5 months of age (n=8, 5M + 3F, for FAD:i-PKM2

and

1139

n=6, 2M + 4F, for FAD:i-PKM2

MΔ

mice).

(H)

Average size distribution of plaques in FAD:i-PKM2

1140

and FAD:i-PKM2

M∆

mice at 4-5 months of age (n=6, 5M + 1F, for FAD:i-PKM2

and n=6, 2M + 4F,

1141

for FAD:i-PKM2

MΔ

mice).

(I)

Representative images of active synaptic puncta that were stained with

1142

JNeurosci Accepted Manuscript

vGlut1 and PSD95 in the hippocampus of FAD:i-PKM2

and FAD:i-PKM2

M∆

mice at 4-5 months of

1143

age. Arrows indicate active synaptic puncta with overlapping or closely juxtaposed vGlut1 and PSD95

1144

immunoreactivity.

(J)

Quantification of active synaptic puncta from FAD:i-PKM2

and FAD:i-PKM2

M∆

1145

mice at 4-5 months of age (n=6, 5M + 1F, for FAD:i-PKM2

and n=6, 2M + 4F, for FAD:i-PKM2

MΔ

1146

mice).

Data are expressed as mean ± SEM. Unpaired Student’s

-tests were used in (

E, G,

1147

1148

Figure 5. Selective deletion of Pkm2 in microglia of 5XFAD mice leads to increase in DAM

1149

genes. (A)

Uniform Manifold Approximation and Projection (UMAP) of snRNAseq data from CD11b

1150

microglia purified from 4-month-old FAD:i-PKM2

and FAD:i-PKM2

M∆

mice (n=3, 2M + 1F, for each

1151

genotype). Microglia are represented in 6 clusters, designated 0, 1, 2, 3, 4, and 6. Based on gene

1152

expression profiles, clusters of 0, 1, and 3 represent homeostatic microglia, while clusters 4 and 6

1153

represent disease-associated microglia. Cluster 2 comprises intermediate microglia.

Extended Data,

1154

Figure 5-1.

Metadata of genes identified by snRNAseq with cluster designation.

(B)

Proportion of

1155

cells identified in each cluster.

(C)

Violin plot showing

Pkm

gene expression in each cluster.

Extended

1156

Data, Figure 5-2.

List of differentially expressed genes between two genotypes with fold changes.

1157

(D)

Volcano plots of selected genes whose log

fold change is either greater than +0.20 or less than

1158

-0.20 among microglial clusters (n=484 genes). Genes on the right of the volcano plot represent genes

1159

whose expression is increased in FAD:i-PKM2

M∆

mice compared to FAD:i-PKM2

mice. DAM genes

1160

are indicated with pink arrows.

Extended Data, Figure 5-3.

List of genes utilized for a Volcano plot.

1161

(E)

UMAP analysis of

ApoE

and

TREM2

genes in FAD:i-PKM2

and FAD:i-PKM2

M∆

mice. Clusters

1162

of 4 and 6 are indicated.

(F)

Western blots of APOE and TREM2 proteins from CD11b

microglia

1163

JNeurosci Accepted Manuscript

isolated from 4-month-old FAD:i-PKM2

and FAD:i-PKM2

M∆

mice.

(G)

Quantification of the blots in

1164

(F) (n=3, 2M + 1F for FAD:i-PKM2

and n=3, 1M + 2F for FAD:i-PKM2

M∆

mice).

Data are expressed

1165

as mean ± SEM. Unpaired Student’s

-tests were used om (G).

1166

1167

Figure 6. Selective microglia deletion of Pkm2 in 5XFAD mice leads to activation of DAM

1168

phenotype in microglia. (A)

Heatmap analysis of DAM genes in all microglial clusters plus the

1169

macrophage cluster 5 in FAD:i-PKM2

mice.

(B)

Heatmap analysis of DAM genes in clusters 2, 4,

1170

and 6 in both genotypes.

(C)

Heatmap analysis of AD risk genes in all microglial clusters in FAD:i-

1171

PKM2

mice.

(D)

Heatmap analysis of AD risk genes in clusters 2, 4, and 6 in both genotypes.

(E)

1172

Pie diagrams of the proportion of homeostatic and DAM genes expressed in microglial clusters of 2,

1173

4, and 6 from both genotypes.

(F)

Pie diagrams showing the proportion of homeostatic and DAM

1174

genes whose expression levels were changed upon

Pkm2

deletion in clusters of 2, 4, and 6.

(G)

1175

qPCR analyses of selected DAM and AD risk genes which were identified to show changes in mRNA

1176

levels (n=3, 2M + 1F for each genotype ).

Data are expressed as mean ± SEM. Unpaired Student’s

1177

-test was used in (G).

1178

1179

Figure 7. The cluster 6 is distinct from other clusters. (A)

Ingenuity Pathway analysis of the top

1180

15 canonical pathways of clusters 0 to 6 in FAD:i-PKM2

and FAD:i-PKM2

M∆

mice.

(B)

Western blots

1181

of p-

AMPKα, AMPKα, p-ACC, and ACC from CD11b

microglia isolated from 4-month-old FAD:i-

1182

PKM2

and FAD:i-PKM2

M∆

mice.

(C)

Quantification of the blots in (B) (n=3, 1M + 2F for FAD:i-

1183

JNeurosci Accepted Manuscript

PKM2

and n=3, 2M + 1F for FAD:i-PKM2

M∆

mice). Data are expressed as mean ± SEM. Unpaired

1184

Student’s

-test was used in (C).

1185

1186

Figure 8. The cluster 6 is enriched with phagocytic genes.

(A)

Identification of ligand-receptor

1187

pairs whose interactions were significantly different between the genotypes. Note that genes in black

1188

were not significant, while those in blue and red were significantly enriched in FAD:i-PKM2

and

1189

FAD:i-PKM2

M∆

mice, respectively.

(B)

UMAP analysis of

Axl

gene in FAD:i-PKM2

and FAD:i-

1190

PKM2

M∆

mice. Clusters of 4 and 6 are indicated.

(C)

Violin plot showing

Axl

gene expression in each

1191

cluster.

(D)

Comparison of Axl signaling between FAD:i-PKM2

and FAD:i-PKM2

M∆

mice. Edge

1192

colors are consistent with the sources of sender and the thicker edge lines indicate stronger signals.

1193

(E)

Representative images showing the colocalization of Axl and PKM2 in plaque-associated

1194

microglia in 5XFAD mice (left). Imaris 3D reconstruction image is also shown (right).

(F)

Western blots

1195

of p-Axl and Axl from CD11b

microglia isolated from FAD:i-PKM2

and FAD:i-PKM2

M∆

mice.

(G)

1196

Quantification of the p-Axl/Axl levels in (F) (n=3, 1M + 2F for FAD:i-PKM2

and n=3, 2M + 1F for

1197

FAD:i-PKM2

M∆

mice). Data are expressed as mean ± SEM. Unpaired Student’s

-test was used.

(H)

1198

Expression levels of genes involved in phagocytosis in

microglial/macrophage clusters in FAD:i-

1199

PKM2

mice.

1200

1201

Figure 9. Pkm2 deletion in microglia resulted in increase in glycolytic and respiratory genes.

1202

(A)

Heatmap analyses of genes for glycolysis in all microglial clusters plus the macrophage cluster 5

1203

JNeurosci Accepted Manuscript

in FAD:i-PKM2

mice.

(B)

Heatmap analyses of genes for glycolysis in clusters 2, 4, and 6 in both

1204

genotypes.

(C)

Heatmap analyses of genes for TCA cycle in all microglial clusters plus the

1205

macrophage cluster 5 in FAD:i-PKM2

mice.

(D)

Heatmap analyses of genes for TCA cycle in

1206

clusters 2, 4, and 6 in both genotypes.

(E)

Western blots of selected glycolytic enzymes from CD11b

1207

microglia isolated from 4-month-old FAD:i-PKM2

and FAD:i-PKM2

M∆

mice. HK1, hexokinase 1;

1208

HK2, hexokinase 2; PFKFB3, 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3; PFKP,

1209

phosphofructokinase, platelet; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; LDHa, lactate

1210

dehydrogenase a; PDH, pyruvate dehydrogenase.

(F)

Quantification of the blots in (E) (n=3, 1M + 2F

1211

for FAD:i-PKM2

and n=3, 2M + 1F for FAD:i-PKM2

M∆

mice).

(G)

Western blots of CS, citrate

1212

synthase and MDH2, malate dehydrogenase 2 from CD11b

microglia isolated from 4-month-old

1213

FAD:i-PKM2

and FAD:i-PKM2

M∆

mice.

(H)

Quantification of the blots in (G) (n=3, 1M + 2F for FAD:i-

1214

PKM2

and n=3, 2M + 1F for FAD:i-PKM2

M∆

mice).

Unpaired Student’s

-test was used in (

and

1215

1216

Figure 10. Pkm2 deletion in microglia resulted in increase in glycolytic and respiratory

1217

capacity. (A)

A representative graph showing the measurement of ECAR in cultured CD11b

1218

microglia isolated from 6-week-old FAD:i-PKM2

and FAD:i-PKM2

M∆

mice.

(B)

Quantification of the

1219

changes in glycolysis, glycolytic capacity, and glycolytic reserve by

Pkm2

deletion from (A) (n=5

1220

replicates for each genotype in n=2 independent experiments).

(C)

A representative graph showing

1221

the measurement of OCR in cultured CD11b

microglia isolated from 6-week-old FAD:i-PKM2

and

1222

FAD:i-PKM2

M∆

mice.

(D)

Quantification of the changes in basal and maximal respiration, and spare

1223

respiratory capacity by

Pkm2

deletion from (C) (n=5 replicates for each genotype in n=2 independent

1224

JNeurosci Accepted Manuscript

experiments).

Data are expressed as mean ± SEM. Unpaired Student’s

-test was used.

(E)

1225

Representative transmission electron microscopy (TEM) images showing the structure of

1226

mitochondria in CD11b

microglia freshly isolated from 4-month-old FAD:i-PKM2

and FAD:i-

1227

PKM2

M∆

mice.

(F)

Violin plot showing quantification of the average cristae area per mitochondria in

1228

(E).

(G)

Violin plot showing quantification of the average cristae length per mitochondria in (E). Data

1229

are expressed as mean ± SEM. Unpaired Student’s

-test was used in (B, D, F, G).

1230

1231

Figure 11. In situ spatial metabolomics assays to measure metabolite abundance among

1232

microglia that are associated with plaques. (A)

Representative matrix-assisted laser

1233

desorption/ionization mass spectrometry (MALDI-MSI) images showing the abundance of pyruvate in

1234

the sagittal brain sections of FAD:i-PKM2

and FAD:i-PKM2

M∆

mice at 4-5 months of age.

(n=3, all

1235

F, for each genotype).

(B)

Representative MALDI-MSI images showing the overlay of the abundance

1236

of pyruvate among Iba1

, AmyloGlo

plaque-associated microglia in the hippocampus of FAD:i-

1237

PKM2

and FAD:i-PKM2

M∆

mice.

(C)

Violin plot showing quantification of the relative abundance of

1238

pyruvate in Iba1

, AmyloGlo

plaque-associated microglia in selected cortex, hippocampus, and

1239

subiculum regions.

(D-F)

Violin plots showing quantification of the relative abundance of metabolites

1240

involved in glycolysis and TCA cycle

(D)

, in nucleic acid metabolism

(E)

, and in lipid metabolism as

1241

well as and other metabolites

(F)

. Data are expressed as mean ± SEM. Unpaired two-

tailed Student’s

1242

-tests were used (B, D, E, F).

1243

1244

JNeurosci Accepted Manuscript

Figure 12. In situ spatial metabolomics analyses revealed an increase in the flux of TCA

1245

metabolites in plaque-associated microglia in the absence of Pkm2. (A)

Summary of

1246

the changes of glycolytic and TCA metabolites in Iba1

, AmyloGlo

plaque-associated microglia

1247

quantified by MALDI-MSI in FAD:i-PKM2

M∆

mice compared to FAD:i-PKM2

mice.

(B)

qPCR

1248

analyses showing changes in

ApoE

mRNA levels after metabolic stress in rat primary microglial

1249

cultures (n=9 replicates for each treatment from 3 independent experiments).

(C)

qPCR analyses

1250

showing changes in

Trem2

mRNA levels after metabolic stress in primary rat microglial cultures (n=9

1251

replicates for each treatment from 3 independent experiments). Data are expressed as mean ± SEM.

1252

Unpaired Student’s

-tests were used in (

1253

JNeurosci Accepted Manuscript