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A locked immunometabolic switch underlies TREM2 R47H loss of function in human iPSC--derived microglia

Loss-of-function genetic variants of triggering receptor expressed on myeloid cells 2 (TREM2) are linked with an enhanced risk of developing dementias. Microglia, the resident immune cell of the brain, express TREM2 and microglial responses are implicated in dementia pathways. In a normal surveillance state, microglia use oxidative phosphorylation for their energy supply, but rely on the ability to undergo a metabolic switch to glycolysis to allow them to perform rapid plastic responses. We investigated the role of TREM2 on microglial metabolic function in human patient iPSC-derived-microglia expressing loss of function variants in TREM2. We show that these TREM2 variant iPSC-microglia, including the Alzheimer’s disease R47H risk variant, exhibit significant metabolic deficits including a reduced mitochondrial respiratory capacity and an inability to perform a glycolytic immunometabolic switch. We determined that dysregulated PPARγ/p38MAPK signalling underlies the observed phenotypic deficits in TREM2 variants and that activation of these pathways can ameliorate the metabolic deficit in these cells and consequently rescue critical microglial cellular function such as β-Amyloid phagocytosis. These findings have ramifications for microglial focussed-treatments in AD.


Introduction
Microglia are the tissue resident macrophages and innate immune cells of the brain and need to rapidly respond to changes in their environment. Quiescent, surveillant innate immune cells such as microglia present in normal brain respond to activating stimuli by reprogramming metabolism, specifically, switching to favour glycolysis over oxidative phosphorylation (Kelly & O'Neill, 2015). Whilst glycolysis is an inefficient way to generate ATP, this switch enables microglia to undergo the rapid changes associated with their enhanced plasticity of function (Orihuela et al, 2016). It is also the preferential mechanism in proliferative cells to facilitate the uptake and incorporation of essential nutrients vital for the production of new cellular biomass (Vander Heiden et al, 2009).
In neurodegenerative diseases, aberrant microglial function has been increasingly recognised to contribute to disease progression however the influence of the cellular metabolic phenotype is not well understood. Missense mutations of triggering receptor expressed on myeloid cells 2 (TREM2) are associated with an enhanced risk of developing dementias including late onset Alzheimer's disease (LOAD) (Guerreiro et al, 2013;Jonsson et al, 2013). In the CNS, TREM2 is exclusively expressed in microglia, and numerous studies have linked the disease-associated mutations to deficits in microglial function, including ligand binding/sensing, phagocytosis, and inflammatory responses (Kleinberger et al, 2014;Wang et al, 2015).
Much of the current work to elucidate the loss of function consequences of TREM2 variants in AD has employed the use of KO animal models and whilst a role for TREM2 has been described in microglial metabolism (Ulland et al 2017), it is not known whether disease-relevant variants also harbour metabolic deficits or the nature of any observed deficits. Here, we used human iPSC-derived microglia (iPS-Mg) generated from donors harbouring specific TREM2 mutations previously characterised as hypomorphic variants in Alzheimer's disease and Nasu Hakola disease (NHD), and identified deficits in microglial metabolic regulation and associated functions. Furthermore, we identified for the first time that TREM2 variants are unable to carry out an immunometabolic switch to induce glycolysis and that this depends on PPARγ-p38MAPK-PFKFB3 signalling.

Materials and Methods iPSC generation
Ethical permission for this study was obtained from the National Hospital for Neurology and Neurosurgery and the Institute of Neurology joint research ethics committee (study reference 09/H0716/64) or by the Ethics Committee of Istanbul Faculty of Medicine, Istanbul University (for collection of T66M mutant fibroblasts to Dr Ebba Lohmann). R47H heterozygous fibroblasts were acquired with a material transfer agreement between University College London and University of California Irvine Alzheimer's Disease Research Center (UCI ADRC; M Blurton-Jones). Fibroblast reprogramming was performed by episomal plasmid nucleofection (Lonza) as previously described (Okita et al, 2011), using plasmids obtained from Addgene (#27077, #27078 and #27080). Nucleofected cultures were transferred to Essential 8 medium (Life Technologies) after 7 days in vitro (DIV) and individual colonies were picked after 25-30 DIV and CNV analysis performed. All iPSCs were maintained and routinely passaged in Essential 8 medium. Karyotype analysis was performed by The Doctors Laboratory (London, UK) ( Supplementary Fig 2B-D). The R47H hom line was a gene-edited isogenic of BIONi010-C, purchased from EBiSC (BIONi010-C7). Control iPSC lines used in this study are as follows: CTRL1 (kindly provided by Dr Selina Wray, UCL Institute of Neurology); CTRL2 (SBAD03, Stembancc); CTRL3 (SFC840, Stembancc); CTRL4 (BIONi010-C, EBiSC).

Cellular Stress Proteome array
Cells were treated for 8 hours with 2-deoxyglucose (2-DG; 3mM) and cell lysates were prepared as per the manufacturer's instructions (Proteome Profiler™ Human cell stress array; Bio-Techne). Total protein quantification was performed on aliquots of each treatment group for data normalisation purposes. Lysates were pooled from 3 independent experiments, according to TREM2 genotype after basal or 2-DG treatment. Data were analysed using the Protein Array Analyser Palette plugin for ImageJ (Carpentier & Henault, 2010), and plotted as relative protein expression, normalised to total cellular protein levels.
Immunoblotting iPS-Mg were lysed in RIPA buffer (50 mM Tris, 150mM NaCl, 1% SDS, and 1% Triton X-100) containing 1x Halt™ protease and phosphatase inhibitor cocktail. Lysates were separated into soluble and insoluble (nuclear) fractions. Samples were resolved and transferred onto nitrocellulose membranes and incubated with primary and secondary antibodies (Table 1). Blotting was visualised on an Odyssey detection system (LiCor) and quantified using ImageJ software (www.imagej.nih.gov/ij).

Live cell morphological staining
iPS-Mg were matured on 13mm glass coverslips. Cells were treated with 100ng/ml LPS and 10U/ml human IFNγ for 24 hours prior to staining. Prior to visualisation, cells were incubated with 1 µM Calcein-AM for 15 minutes at 37°C. Images were captured on a Zeiss Axioskop 2 fluorescence microscope and image analysis was carried out with Axiovison 4.8 and ImageJ software.

Mitochondrial superoxide analysis
Basal levels of mitochondrial superoxide were measured by loading cells with MitoSOX™ red superoxide indicator (Thermo Fisher) and analysed by flow cytometry. Briefly, a 5µM working concentration of MitoSOX™ red was prepared in warmed FACs buffer (PBS + 0.5% BSA). Cells (400,000) were incubated with MitoSOX™ red for 10 minutes at 37°C, protected from light. Cells were washed twice and resuspended in FACs buffer followed by flow cytometry (FL2; FACs Calibur, Beckton Dickinson). Incubation with Rotenone (100nM) for 30 minutes was used a positive control. Data were analysed using Flowing Software v2.5.1 (University of Turku).

Mitochondrial number determination
To assess gross mitochondrial number in the iPS-Mg lines, flow cytometry analysis using MitoTracker™ Green (Thermo Fisher) was performed. Briefly, a 200nM working concentration of MitoTracker™ Green was prepared in warmed FACs buffer (PBS + 0.5% BSA). Cells (400,000) were incubated with MitoTracker™ Green for 30 minutes at 37°C, protected from light. Cells were washed twice and resuspended in FACs buffer followed by flow cytometry (FL1; FACs Calibur, Beckton Dickinson). Data were analysed using Flowing Software v2.5.1 (University of Turku).

PPARγ transcriptional activity
The DNA binding activity of PPARγ was detected in iPS-Mg nuclear extracts as previously described (Jiang et al, 2014) from untreated and groups treated with 2-DG (3mM) for 8 hours, using a PPARγ transcription factor assay kit, as per the manufacturer's instructions (AbCam).

Cellular respiration analysis
For real-time analysis of oxygen consumption rates (OCR) and extracellular acidification rates (ECAR), iPS-Mg were plated and matured on Seahorse cell culture microplates and analysed using a Seahorse XFe96 Analyser (Agilent Technologies). Cells were incubated overnight with or without pioglitazone (100nM) or GW0742 (100nM). Mito stress kits were used to analyse mitochondrial respiration and Glycolytic stress kits were used to analyse cellular glycolysis. Data were analysed using Wave v2.4.0.6 software (Agilent Technologies).

Aβ1-42 (HiLyte488) phagocytosis
Cells were plated and matured at a density of 20,000/well in 24 well plates (2 wells pooled/treatment group). Cells were treated with pioglitazone (100nM) +/-pre-treatment (1 hour prior) with SB202190 (100nM) or AZ-PFKFB3 (50nM) for 24 hours prior to FACs analysis. On the day of the experiment, cells were removed from the incubator to equilibrate to RT for 30 minutes and cytochalasin-D (CytoD; 100µM) was added to negative control groups. Aβ1-42 (HiLyte488; 100nM) was added to all groups except unstained groups and allowed to bind to phagocytic receptors for 30 minutes at RT. Grouped wells were then pooled into 2ml tubes, centrifuged at 300g for 3 minutes at RT, media aspirated, and the cell pellet resuspended in fresh RT maturation medium. Tubes were incubated at 37°C + 5% CO2 for 1 hour to initiate phagocytosis of bound Aβ1-42. Tubes were centrifuged at 300g for 3 minutes at RT, media was aspirated, and cell pellets were resuspended in PBS for FACs analysis (FL1; FACs Calibur, Beckton Dickinson). Data were analysed using FCS Express 6 Plus (DeNovo Software). T66M/W50C) that give rise to differential TREM2 protein glycosylation and cleavage (Fig 1A). Cellular respiration by Seahorse analysis of the oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) was investigated in all available lines. Under mitochondrial stress and glycolytic stress, deficits were observed in oxidative phosphorylation (Fig 1Bi) and glycolytic function (Fig 1Bii) respectively. Specifically, levels of maximal respiration (Fig 1C), glycolysis (Fig 1D) and glycolytic capacity (Fig 1E) were significantly reduced in all iPS-Mg harbouring TREM2 variants compared with control lines. We confirmed that respiratory deficits were not due to underlying differences in the parental iPSC lines (Fig 1F); the results indicate that all lines prior to differentiation (and before TREM2 expression) displayed metabolic phenotypes independent of TREM2 genotype. We also measured mitochondrial number, since it has been reported that TREM -/mice exhibit reduced mitochondrial mass (Ulland et al., 2017), however we found no difference as measured by MitoTracker Green between control and TREM2 variant iPS-Mg lines (Fig 1G). Instead, production of mitochondrial superoxide assessed by MitoSOX red was increased in both AD R47H and NHD TREM2 iPS-Mg (Fig 1Hi & Hii) suggesting dysregulation of basal mitochondrial function in TREM2 mutant carriers.
iPS-Mg with TREM2 hypomorphic variants fail to "switch" to glycolysis following an inflammatory challenge Given the energetic deficits in TREM2 variant iPS-Mg in response to mitochondrial and glycolytic stress, we investigated how these cells respond to an inflammatory challenge. We found that following exposure to lipopolysaccaride (LPS) and interferon-gamma (IFNγ), control iPS-Mg undergo morphological changes, specifically increased cellular elongation and a reduction in ramified processes (Fig 2A), and show substantial release of the pro-inflammatory cytokine TNFα (Fig 2B). In TREM2 variant iPS-Mg, the morphological changes are less dramatic (Fig 2A) and they exhibit a significant reduction in TNFα release compared with control lines (Fig 2B), indicating a deficit in the microglial response to the inflammatory stimulus. Microglia respond to pro-inflammatory stimuli by initiating a metabolic switch from oxidative phosphorylation to glycolysis. Seahorse analysis was again employed to measure OCR and ECAR, this time following exposure to LPS and IFNγ. Control iPS-Mg showed a robust shift from low quiescent to high energetic respiration in response to LPS and IFNγ (Fig 2C). In contrast, TREM2 variant iPS-Mg from AD and NHD lines remained "stuck" in a quiescent respiratory state (Fig 2C). When glycolysis was blocked with 2-deoxyglucose (2-DG, 3mM) prior to LPS/IFNγ, control iPS-Mg are still able to generate aerobic energy (Fig 2D) to compensate. Interestingly, R47H het iPS-Mg were also able to induce aerobic respiration following inhibition of glycolysis, unlike the other TREM2 homozygous variants that remained in a quiescent state (Fig 2D).

TREM2 variant iPS-Mg induce cellular stress pathways in response to glycolytic inhibition and exhibit reduced PPARγ signalling
In order to investigate the comparable aerobic responses after glycolytic inhibition, but underlying phenotypic differences between the control and patient AD R47H het iPS-Mg, cells were treated with 2-DG and probed for changes in key cell stress pathways. In 2-DG-treated R47H het iPS-Mg we identified enhanced levels of cell stress proteins that are linked to energy metabolism and/or mitochondrial function, including HIF1α, cytochrome-c, Bcl-2, Cited-2, NF-κB and p38α MAPK (Fig 3Ai & 3Aii). PGC-1α, the master regulator of mitochondrial biogenesis and energy metabolism (Liang & Ward, 2006; Austin & St-Pierre, 2012), is downstream of a number of these pathways and so we examined phosphorylation levels at serine 571, which negatively regulates PGC-1α activity in control and TREM2 variant iPS-Mg. We found that phosphorylation at serine 571, was significantly enhanced in TREM2 variant iPS-Mg, suggesting that PGC-1α activity is downregulated in TREM2 variant cells (Fig 3Bi &   Bii). PGC-1α is also a co-activator with the nuclear receptor and transcription factor PPARγ widely expressed in inflammatory cells such as microglia and macrophages as well as adipose tissue where it controls inflammation, lipid metabolism and glucose homeostasis (Corona & Duchen, 2016). Following 2-DG treatment, we found increased PPARγ transcriptional activity in control iPS-Mg but no increase in AD R47H het or NHD T66M hom iPS-Mg (Fig 3C) despite the increase in cell stress pathways in AD R47H het iPS-Mg. When we looked at total PPARγ protein levels in iPS-Mg, we found that PPARγ protein levels were significantly reduced in TREM2 hypomorphs, either at basal (T66M hom /W50C hom ) or after glycolytic inhibition with 2-DG (R47H het/hom and T66M het ) (Fig 3Di & Dii).

A PPARγ agonist can rescue energy deficits and the metabolic glycolytic switch in TREM2 variant iPS-Mg
We set out to determine whether dysregulation of PPARγ signalling was the cause of the inability of TREM2 hypomorphs to switch to an energetic phenotype and whether this could be rescued by targeting PPARγ activation. We thus investigated whether oxidative phosphorylation and glycolysis could be modulated through PPARγ agonism; pre-incubation with the PPARγ agonist pioglitazone significantly attenuated the observed energy deficits in maximal respiration (Fig 4Ai & 4Aii), glycolysis (Fig 4Bi & 4Bii), and glycolytic capacity (Fig 4Bi & 4Biii) in iPS-Mg from R47H het/hom carriers, but interestingly did not significantly enhance maximal metabolic respiration in control iPS-Mg, suggesting that metabolic function in these cells was already optimal or saturated. In the case of the T66M hom , or W50C hom TREM2 hypomorphic iPS-Mg, whilst there was a trend to increased respiration with pioglitazone, this was not significant (Fig 4Aii) and pioglitazone did not enhance glycolysis in these cells either (Fig 4Bii & 4iii). Since the R47H variant expressing iPS-Mg were most amenable to rescue with pioglitazone treatment, we examined whether this activation could promote the switch to an energetic phenotype following inflammatory stimulation with LPS and IFNγ; indeed this was found to be the case (Fig 4C) suggesting we can reverse the metabolic deficit in these cells.

PFKFB3 signalling
To further investigate the mechanisms by which pioglitazone can initiate the switch to glycolysis in regulates glucose metabolism via synthesis of fructose-2,6-bisphosphate, a potent allosteric activator of 6-phosphofructo-1-kinase (PFK-1), which catalyses the commitment step of glycolysis through conversion of fructose 6-phosphate and ATP to fructose 1,6-biphosphate and ADP (Li et al, 2018). The signaling pathway leading to activation of PFKFB3 is through p38-MAPK dependent phosphorylation of MK2 and subsequent phosphorylation of PFKFB3 for induction of the glycolytic switch (Bolaños, 2013;Novellasdemunt et al, 2013). We again used LPS/IFNγ to induce glycolysis in iPS-Mg and found increased phosphorylation of MK2 and total protein levels of PFK3B3 that is dependent on p38-MAPK activity. However, in R47H hom iPS-Mg there is a substantial deficit in the ability of LPS/IFNγ to induce phospho-MK2, again suggesting these cells cannot induce glycolysis after an inflammatory challenge. Given that p38-MAPK was increased in R47H in response to glycolytic stress (Fig 3A), it was surprising that p38-MAPK-dependent phosphorylation of MK2 was decreased in response to LPS/IFNγ. We therefore investigated the effect of PPARγ activation by pioglitazone on basal p38MAPK in unstimulated iPS-Mg. We found strong induction of pT180/Y182-p38MAPK by western blot after pioglitazone exposure (100nM) in TREM2 variant iPS-Mg compared with control lines, suggesting that PPARγ induces a significant activation of the p38-MAPK pathway to rescue glycolysis in TREM2 lines that is not observed in control lines. Activation of PPARδ/β by GW0742 was ineffective at increasing p38MAPK phosphorylation (Fig 5Bii), suggesting this pathway is specific to PPARγ. We also probed PFK-1 activity downstream of PFKFB3 and show that pioglitazone is able to enhance PFK-1 activity, but only in the TREM2 hypomorphic lines and that this is p38-MAPK dependent (Fig 5Ci-Ciii), again supporting the idea that TREM2 variants can be rescued via activation of a p38-MAPK pathway to induce glycolysis. Indeed, when we probed glycolysis by Seahorse in R47H hom iPS-Mg, inhibition of p38MAPK blocked the rescue of cellular glycolytic function by pioglitazone activation of PPARγ (Fig 5D).

Activation of the metabolic glycolytic switch by pioglitazone rescues the deficit in phagocytosis of Aβ1-42 in TREM2 variant iPS-Mg
Since pioglitazone can rescue the metabolic switch to glycolysis in TREM2 variant iPS-Mg, we asked whether PPARγ-p38MAPK/PFKFB3 activation can also rescue TREM2-dependent cellular functions. TREM2 has been shown to be important for phagocytosis, a crucial function of microglia that is found to be aberrant in neurodegenerative diseases such as Alzheimer's (Kleinberger et al, 2014;Yeh et al, 2016). We have shown previously that NHD-associated TREM2 variant iPS-Mg have a deficit in phagocytosis of apoptotic cells (Garcia-Reitboeck et al, 2018) however this is not observed in R47H (data not shown). We therefore investigated the ability of TREM2 variants to clear amyloid beta (Aβ), which accumulates in plaques in AD and is the major pathological hallmark of the disease. Here we show that the R47H and the NHD variants exhibit a substantial deficit in their ability to phagocytose oligomeric Aβ1-42 compared with control cells (Fig 6A-C). Since phagocytosis has been shown to be driven in macrophages by glycolytic metabolism (Jiang et al, 2016), we determined whether pioglitazone could reverse the reduced phagocytosis of Aβ in TREM2 variant iPS-Mg. Activation of PPARγ by pioglitazone significantly rescues the TREM2 variant deficit in R47H lines, and this increase in phagocytosis is dependent on p38MAPK signalling (Fig 6A & 6B) and PFKFB3 activity (Fig 6A & 6C). These data suggest that the recovery of the glycolytic switch by activation of PPARγ-p38MAPK/PFKFB3 signalling is sufficient to rescue TREM2-dependent microglial functions such as phagocytosis of Aβ in AD.

Discussion
Recent findings in mouse models suggest that TREM2 plays a role in maintaining microglial metabolic fitness (Ulland et al, 2017) and that TREM2-modulates the metabolic homeostasis of adipose tissue associated macrophages specifically the homeostasis of glucose, insulin, cholesterol, HDL and LDL (Jaitin et al, 2019) as well as disease-associated microglia in AD (Keren-Shaul et al, 2017).
Our data point to significant deficits in iPS-Mg harbouring TREM2 hypomorphic variants when in a basal homeostatic state. Specifically, these lines exhibit reduced maximal mitochondrial respiratory capacity as well as reduced glycolytic capacity when compared with common variant controls. We confirm these deficits correspond to the presence of TREM2 variants and are not an inherent property of the iPSC since the corresponding undifferentiated cells do not show TREM2 genotype-dependent metabolic deficits. These energy deficits are not due to lower mitochondrial numbers, but may be influenced by enhanced mitochondrial superoxide levels. Indeed, mitochondrial impairment, namely energy deficiencies and aberrant ROS signalling, have been previously linked to a number of neurodegenerative diseases (Lin & Beal, 2006), most notably Parkinson's disease (Park et al, 2018) but also AD and ageing (Trifunovic & Larsson, 2008;Sahin & Depinho, 2010;Wang et al, 2014). Our current findings are the first to show these metabolic deficits in disease relevant TREM2 hypomorphic microglia, however, whilst microglia upon activation can produce a burst of reactive oxygen species via the activity of the NAPDH oxidase (Bal-Price et al, 2002;Brown & Vilalta, 2015) it remains to be seen whether the production of cellular superoxide is altered in TREM2 hypomorphic microglia.
We identified aberrant responses following exposure to classical pro-inflammatory stimuli (LPS/IFNγ) in TREM2 hypomorphic iPS-Mg; specifically, reduced morphological changes and TNFα release. Both responses require rapid energy production which is produced when microglia undergo the metabolic switch to glycolysis (Finucane et al, 2019;Jiang et al, 2016). Glycolysis allows energy production and uptake of essential nutrients to support the rapid changes required by "activated" microglia in response to a stimulus, such as phagocytosis, proliferation, migration and induction of protein synthesis for cytokine and chemokine secretion (Vander Heiden et al, 2009;Shen et al, 2017;Gu et al, 2017;Culmsee et al, 2018). Indeed, we identified the ability of the TREM2 variant expressing iPS-Mg to undergo a normal switch in metabolism from a homeostatic, surveillance profile supported by oxidative phosphorylation, to one in which glycolysis is impaired. Interestingly, when we blocked glycolysis prior to LPS/IFNγ exposure, only control and R47H het iPS-Mg were able to increase energy demand through enhanced oxidative phosphorylation. These data suggest a less dramatic loss of energy production in the AD-associated risk variant compared with the more severe NHD mutations.
The identification of comparable aerobic responses after glycolytic inhibition, but underlying metabolic and phenotypic differences between the control and patient AD R47H het iPS-Mg, led us to probe the effect of glycolytic inhibition on cell stress pathways. Indeed, previous studies have shown that glycolytic inhibition can enhance oxidative stress (Shutt et al, 2010;Sharma et al, 2010), and with increased mitochondrial ROS levels in the TREM2 hypomorphic lines, we hypothesised that the discrepancies identified in the R47H het lines may be due to a hyper-sensitivity to glycolytic inhibition. We found increased expression in the number of proteins indicative of metabolic and mitochondrial stress pathways in the R47H het iPS-Mg when compared with control iPS-Mg supporting this hypothesis.
Furthermore, when we investigated PGC1α, a master regulator of mitochondrial biogenesis and energy metabolism and signalling molecule downstream of a number of the pathways identified, we found enhanced negative regulation in the TREM2 hypomorphic lines. Collectively, these data suggest that whilst cell stress pathways are enhanced in R47Hhet lines during glycolytic inhibition, the cells' ability to respond is downregulated. Indeed, when we looked at the transcriptional activity of PPARγ, a nuclear receptor and transcription factor co-activated by PCG-1α, we found that control iPS-Mg were able to respond to glycolytic inhibition whereas R47H het and T66M hom iPS-Mg were locked and unable to respond. PPARγ is widely expressed in inflammatory cells such as microglia and macrophages as well as adipose tissue where it controls inflammation, lipid metabolism and glucose homeostasis (Corona & Duchen, 2016). In support of the aberrant PPARγ signalling, we found protein levels were highly regulated by glycolytic inhibition, specifically in R47H lines and the heterozygous variant of T66M.
Interestingly basal levels of PPARγ were significantly reduced in the severe NHD lines.
PPARγ, also known as the glitazone receptor or nuclear receptor subfamily 3 (NR1C3) is involved in insulin sensitisation and enhanced glucose metabolism, and plays a key role in cellular homeostasis (Tyagi et al, 2011). The prominent isoform expressed in inflammatory cells is PPARγ3. Pioglitazone, an agonist of PPARγ, has been shown to halt progression of Parkinsonism in a rodent model, ostensibly by inhibiting microglial inflammation and proliferation (Machado et al, 2019), although it is difficult to conclude if this is a direct effect on microglia given that the compound can affect a number of cells in these models. Targeting PPARγ for therapeutic benefit in AD has also recently been proposed (Khan et al, 2019). Interestingly we found that whilst the deficits in maximal respiration, glycolysis and glycolytic capacity observed in the R47H variants can be rescued by the PPARγ agonist, these deficits in the NHD hypomorphs cannot, suggesting that the reduced levels of PPARγ protein observed in these variants and the limited levels of mature TREM2 may have an uncoupling effect on the signalling to and from PPARγ. Furthermore, pre-incubating R47H het iPS-Mg with pioglitazone prior to LPS/IFNγ exposure rescued the energetic metabolic phenotype, strongly linking PPARγ function to the locked immunometabolic switch observed in TREM2 hypomorphic lines.
PPARγ signalling has been identified as vital in cellular immune responses. Specifically, PPARγ activation has been shown to inhibit the expression of inflammatory cytokines and promote antiinflammatory phenotypes (Ji et al, 2001;Clark, 2002). Whilst PPARγ expression is reduced in NHD TREM2 hypomorphs, one might expect a correspondingly higher level of TNFα secretion if PPARγ is controlling inflammation, however the opposite occurs. Whilst it may be true for TNFα, despite the requirement for the metabolic switch to glycolysis upon microglial activation for energy to produce cytokines and chemokines, we found previously that the ability of iPS-Mg to produce a whole range of cytokines was not greatly impaired when a stimulus of LPS alone was used (Garcia-Reitboeck et al, 2018). Here we primed with IFNγ and LPS, and were able to show a deficit in the secretion of TNFα in TREM2 hypomorphs compared with controls. TNFα and IL-6 expression may be controlled by activating transcription factor 3 (ATF3) following TLR stimulation or NFĸB, and at least three classes of transcription factor and coregulators are thought to control the inflammatory secretome (Medzhitov & Horng, 2009). Of further interest would be to determine the effects of multiple exposures to stimulants likely to evoke cytokine release in control iPS-Mg, to determine whether there are deficiencies or enhancements in TREM2 variant iPS-Mg.
To understand how PPARγ signalling influences the metabolic phenotype of the TREM2 variants we interrogated PFKFB3 signalling, a key regulatory enzyme involved in glycolytic induction (Rodríguez-García et al, 2017;Finucane et al, 2019) and identified as a target of PPARγ (Guo et al, 2010). We found that pioglitazone exerted its effects via a p38MAPK/PFKFB3 signalling cascade and we show that activation of the PPARγ/p38MAPK cascade and PFKFB3 activity is sufficient to rescue the functional deficit in Aβ1-42 phagocytosis identified in the TREM2 hypomorphic iPS-Mg, a key hallmark associated with AD pathogenesis. Finally, it is worth commenting on previous studies that identify p38-MAPK activation by pioglitazone (Xing et al, 2008;Ji et al, 2010). However, these studies use the compound at concentration 15 to 30x higher than our treatment, which itself is 7x lower than the reported EC50 for PPARγ activity.
In conclusion, we find that the presence of a TREM2 mutation detrimentally influences metabolic signalling by influencing not only basal levels of oxidative phosphorylation but also the requirement of a metabolic switch to glycolysis. This inability to switch on glycolysis during a change in environment significantly impacts upon the surveillant properties of homeostatic microglia leading to sub-optimal responses in key microglial functions such as phagocytosis. Our data highlight these deficits in a human, genetically disease relevant microglial model and uncover a significant dependence on p38MAPK signalling during glycolysis in iPS-Mg generated from R47H genotypes. This polymorphism seems to render the microglia hyper-sensitive to cellular stress, however this susceptibility enhances the effectiveness of PPARγ activation in upregulating cellular metabolism, leading to an ability to attenuate deficits in microglial function associated with disease pathogenesis.      C. PFK1 enzyme activity is specifically enhanced by pioglitazone in TREM2 hypomorphic lines when compared to control iPS-Mg, and is dependent on p38MAPK (Ci-Ciii).

D.
Complete traces of the R47H hom iPS-Mg glycolytic stress test show pioglitazone is able to enhance glycolysis and glycolytic function in a P38MAPK-dependent manner.