Microglial reactivity and neuroinflammation-driven changes in motivational behaviors are regulated by Orai1 calcium channels

Kaitlyn E DeMeulenaere; Rogan A Grant; Megan E Martin; Hiam A Valencia; Jelena Radulovic; Michael W Salter; Murali Prakriya

doi:10.1126/scisignal.ady8398

. Author manuscript; available in PMC: 2026 Jul 27.

Published in final edited form as: Sci Signal. 2026 Feb 17;19(925):eady8398. doi: 10.1126/scisignal.ady8398

Microglial reactivity and neuroinflammation-driven changes in motivational behaviors are regulated by Orai1 calcium channels

Kaitlyn E DeMeulenaere ¹, Rogan A Grant ², Megan E Martin ¹, Hiam A Valencia ², Jelena Radulovic ³, Michael W Salter ⁴, Murali Prakriya ^1,^5,^*

PMCID: PMC13401804 NIHMSID: NIHMS2190650 PMID: 41701811

Abstract

Microglia are the brain’s resident immune cells that respond to injury and disease by transitioning between homeostatic and reactive states. These cell state transitions determine whether microglia promote or resolve inflammation in the central nervous system (CNS). In this study, we explored the role of Ca²⁺ signaling in regulating broader microglial cell state transitions and identified Orai1 Ca²⁺ channels as critical regulators of microglial plasticity and neuroinflammatory signaling. Conditional deletion of Orai1 in microglia impaired their ability to adopt reactive, proinflammatory states. Transcriptomic and metabolomic profiling revealed that Orai1 deletion suppressed the expression of proinflammatory genes linked to immunity, inflammation, and cell metabolism. Conversely, Orai1-deficient microglia generated greater amounts of neuroprotective and anti-inflammatory mediators, including BDNF, ARG1, and the mitochondrial metabolite itaconate. In a model of CNS inflammation induced by peripheral lipopolysaccharide (LPS) challenge, microglial Orai1 deletion attenuated microglial and astrocyte reactivity and reduced hippocampal amounts of the proinflammatory cytokines IL-1β and IL-6. Consistent with these cellular changes, microglial Orai1 knockout mice were protected against LPS-induced decreases in motivational behaviors, including impaired reward-seeking and escape behaviors. These findings establish Orai1 channels as key regulators of microglial cell state transitions, linking Ca²⁺ signaling to neuroinflammation and inflammation-driven behavioral dysfunction.

Editor’s summary

Neuroinflammation mediated by microglia, the resident macrophages of the CNS, can initiate and contribute to the progression of neurological disorders. DeMeulenaere et al. found that the Ca2+ channel Orai1 in microglia was critical for depressive-like behaviors arising from neuroinflammation in mice. In response to systemic inflammation outside the CNS, which often induces neuroinflammation, microglia from mice lacking Orai1 in this cell type assumed an anti-inflammatory cell state. Moreover, these mice had reduced inflammation in the hippocampus and did not show the depressive-like behaviors seen in control mice. These results suggest that Orai1 inhibition could prevent maladaptive behaviors after peripheral and CNS inflammation. —Wei Wong

INTRODUCTION

Microglia are the primary resident macrophage-like immune cells of the central nervous system (CNS) that regulate key brain functions such as synaptogenesis, neurogenesis, immune cell recruitment, and inflammation both in the healthy brain and in various neurological syndromes (1, 2). In the healthy brain, homeostatic microglia exhibit small cell bodies and thin, ramified processes that continuously survey the local brain environment. However, in response to stress, injury, or systemic disturbances, microglia rapidly transition into proinflammatory cell states, marked by proliferation, cellular enlargement, process retraction, and heightened phagocytic activity (2, 3). Upon proinflammatory stimulation, microglia also release an array of proinflammatory cytokines [such as interleukin-6 (IL-6), IL-1β, and tumor necrosis factor–α (TNF-α)], which, if left unchecked, can disrupt neuronal physiology, contributing to the onset and progression of neurological disorders, including psychiatric disorders, neurodegeneration, and chronic pain (1, 4–6). Consequently, there is strong interest in uncovering the molecular and cellular mechanisms that regulate the transition of microglia from homeostatic to proinflammatory reactive states.

Microglial reactivity is coordinated by a diverse array of signaling pathways regulated by surface receptors, ion channels, and second messengers (2, 7). Among these, intracellular Ca²⁺ signaling has emerged as a crucial yet understudied regulator of microglial functions (8–11). Unlike electrically excitable neurons, microglia rely on intracellular Ca²⁺ signaling caused by Ca²⁺ release from internal stores or influx from the extracellular space as their primary mode of excitability (10, 11). Ca²⁺ signaling directly stimulates key proinflammatory effector functions, including the release of cytokines, phagocytosis, and gene expression (10–12). Ca²⁺ regulation of these processes is well suited because it can be rapidly stimulated and modulated by other signaling pathways. However, the precise molecular mechanisms by which Ca²⁺ regulates microglial plasticity and the physiological relevance of this regulation are unknown.

A major Ca²⁺ signaling pathway in many cell types is store-operated Ca²⁺ entry (SOCE), a ubiquitous mechanism of Ca²⁺ influx in nonexcitable cells (13). Mediated by the Orai family of Ca²⁺ channels that are characterized by exceptional Ca²⁺ selectivity, voltage independence, and low unitary conductance, SOCE generates sustained intracellular Ca²⁺ elevations ideally suited for driving transcriptional and enzymatic effector cascades (14–16). The predominant isoform, Orai1, plays a critical role in peripheral immune cells, where it is essential for immune cell activation, differentiation, and cytokine production (17). Although Orai1 is broadly expressed in the nervous system in both neurons and glia (13, 18–23), its functions within the brain only now beginning to be elucidated. In particular, Orai1-mediated SOCE is emerging as a key modulator of neuroinflammatory processes. In microglia, Orai1-mediated SOCE stimulates the production of inflammatory cytokines such as IL-6 and TNF-α (21, 22), whereas conditional deletion of Orai1 in microglia ameliorates spinal cord inflammation and tactile hypersensitivity in models of neuropathic pain (22). Orai1 also contributes to Ca²⁺ signaling in astrocytes, where it regulates gliotransmitter release, proinflammatory cytokine generation, and reactive gliosis (18, 23, 24). Together, these studies indicate that Orai1-dependent SOCE coordinates neuroimmune signaling and activation in glia. Nonetheless, the broader role of Orai1 in controlling microglial state transitions, metabolic reprogramming, and behavioral outcomes is unclear.

To address this question, we combined transcriptomics, metabolomics, and in vivo functional analyses to dissect the Orai1-dependent mechanisms that shape microglial plasticity. Our results identify Orai1-mediated Ca²⁺ signaling as a key regulator of proinflammatory transcriptional and metabolic reprogramming in microglia. This reprogramming is critical for amplifying neuroinflammation in vivo and ultimately driving deficits in motivational behaviors.

RESULTS

Orai1 is necessary for SOCE in hippocampal microglia

To investigate regulation of microglial reactivity and inflammation by Orai1 channels, we used a conditional Orai1 knockout mouse generated by crossing Orai1^fl/fl mice with Cx3CR1-Cre/ERT2 mice (25). We focused our cellular mechanistic studies on the hippocampus because microglia are abundant in this brain region, and there is extensive literature on the functions of hippocampal microglia for neuronal and synaptic activity and their contributions to disease (26–29). Additionally, Ca²⁺ signaling supports key functions in hippocampal microglia, including phagocytosis and neuroimmune interactions (9, 12), suggesting that blockade of Orai1 signaling may have a broad impact on effector functions of hippocampal microglia.

Orai1 deletion in microglia was achieved using two approaches. For the in vitro experiments, microglia were isolated from neonatal Orai1^{fl/fl Cx3CR1-Cre/ERT2} mice [postnatal day 3 (P3) to P5] and cultured for 14 days. Orai1 deletion was induced by adding 4-hydroxytamoxifen to the culture medium starting on day 2 (Fig. 1A). For in vivo studies, 5- to 6-week-old adult Orai1^{fl/fl Cx3CR1-Cre/ERT2} mice received intraperitoneal tamoxifen injections, and mice were analyzed after a waiting period of 30 days after the final tamoxifen dose to allow turnover of short-lived, nonmicroglial myeloid cells (Fig. 1B) (30, 31). mRNA measurements from tamoxifen-treated cultured microglia from Orai1^{fl/fl Cx3CR1-Cre/ERT2} mice showed significant loss of Orai1 gene expression with no detectable changes in the other genes encoding SOCE components, namely, Orai2, STIM1, and STIM2 (Fig. 1C). The in vitro–cultured Orai1 conditional knockout (cKO) microglia showed a small but significant reduction in Orai3 expression (Fig. 1C). Deletion of Orai3 has no effect on SOCE or on most immune cell effector responses, whereas Orai1 is the major component of the CRAC channel needed for SOCE (32, 33). Therefore, we do not consider the modest change in Orai3 expression to be meaningful for the effector functions that we examined in this study. Microglia isolated from tamoxifen-administered, Orai1^{fl/fl Cx3CR1-Cre/ERT2} mice also showed loss of Orai1 mRNA with no changes in the expression of the other Orai or STIM isoforms (Orai2, Orai3, STIM1, and STIM2) (Fig. 1D). We will refer to cells and mice from Orai1^{fl/fl Cx3CR1-Cre/ERT2} mice treated with tamoxifen as cKO (Orai1 cKO) cells/mice. Orai1^fl/fl mice (and cells) administered with tamoxifen were used as controls, and, for simplicity, we will refer to these as wild-type (WT) mice or cells in this study.

Fig. 1. — (A) Schematic illustrating isolation of microglia from hippocampi of neonatal mice. 4-Hydroxytamoxifen (1 μM) was added to the culture medium 2 days after cell isolation to induce Orai1 deletion. KO, knockout. (B) Schematic of microglial isolations from adult mice (10 to 12 weeks old). (C) Normalized RNA-seq gene counts for Orai and STIM isoforms in WT and Orai1 cKO neonatal cultured microglia. Each data point represents data from 100 ng of RNA from ~40,000 microglia obtained from one mouse (n = 6 mice per genotype). Data are shown as means ± SEM. FDR-adjusted P values from differential expression analysis. (D) Normalized RNA-seq gene counts for mRNAs encoding Orai (1–3) and STIM (1–2) isoforms in microglia isolated from adult WT and Orai1 cKO mice. (E) SOCE measurements from neonatal microglia. ER Ca²⁺ stores were deleted with Tg (1 μM) in Ca²⁺-free Ringer’s solution, followed by readdition of 2 mM extracellular Ca²⁺. (F and G) Summary of SOCE parameters. (F) Initial rates of Ca²⁺ influx over 12 s after Ca²⁺ readdition, and (G) peak intracellular Ca²⁺ concentrations. n = 102 cells from five WT mice. n = 78 cells from seven Orai1 cKO mice. (H) Representative Ca²⁺ traces showing SOCE in acutely isolated adult microglia. Tg (1 μM) was applied in Ca²⁺-free Ringer’s solution, and 2 mM extracellular Ca²⁺ was added to elicit SOCE. (I and J) Quantification of SOCE in adult microglia. (I) Initial rates of Ca²⁺ influx over 12 s after extracellular Ca²⁺ readdition. (J) Peak Ca²⁺ concentrations. Data are shown as means ± SEM. P values were determined by two-tailed unpaired t test. n = 67 cells from three WT mice. n = 56 cells from three Orai1 cKO mice.

To address the role of Orai1 calcium signaling in microglia, we investigated changes in intracellular Ca²⁺ concentrations arising from deletion of Orai1 using Fura-2. WT hippocampal microglia exhibited SOCE after depletion of endoplasmic reticulum (ER) Ca²⁺ stores with thapsigargin (Tg) (Fig. 1, E and F) with influx rates comparable to those previously seen in spinal microglia (22). The pharmacological properties of SOCE were consistent with Orai1, including blockade by low doses of La³⁺. However, Orai1 cKO microglia showed significant reductions in SOCE with decreases in both the rate of SOCE-mediated Ca²⁺ entry and the peak [Ca²⁺]_i after extracellular Ca²⁺ readdition (Fig. 1, F and G). Similarly, both the rate and magnitude of SOCE in microglia from adult Orai1^{fl/fl Cx3CR1-Cre/ERT2} mice injected with tamoxifen were significantly reduced compared with those in microglia from Orai1^fl/fl mice (Fig. 1, H to J). These results indicate that Orai1 is essential for SOCE in hippocampal microglia. Deletion of Orai1 also reduced Ca²⁺ signaling evoked by the nucleotide neurotransmitter adenosine 5′-triphosphate (ATP; fig. S1, A to C), a potent endogenous activator of microglia that induces multiple microglial responses, including increased expression of inflammation-related genes, process retraction, and enhanced phagocytic capacity (34). Collectively, these results confirm previous findings indicating that Orai1 channels are essential for mediating SOCE and agonist-evoked Ca²⁺ signaling in microglia (22).

Deletion of Orai1 suppresses microglial inflammatory pathways and up-regulates anti-inflammatory and proreparative markers

Orai1 mediates Ca²⁺-dependent gene expression in many immune cells (16). To address the role of Orai1 on Ca²⁺-dependent gene expression in microglia, we used bulk RNA sequencing (RNA-seq) analysis on WT and Orai1 cKO microglia. We first used primary microglia that were treated for 6 hours with a low dose of Tg to deplete ER Ca²⁺ stores and activate SOCE and with phorbol 12,13-dibutyrate (PDBu) to mimic G protein–coupled receptor (GPCR)–driven protein kinase C activation that frequently occurs during receptor stimulation. This approach selectively engages Ca²⁺-dependent transcriptional programs while minimizing Ca²⁺-independent pathways activated by GPCR agonists. RNA-seq analysis revealed widespread differences in gene expression between WT and Orai1 cKO microglia after stimulation with Tg and PDBu (Fig. 2A). Principal components analysis (PCA) showed that genotype and treatment accounted for most (>70%) of the variance, whereas sex had minimal effect, and, therefore, subsequent analysis was performed irrespective of sex (fig. S2A). At baseline, unstimulated Orai1 cKO microglia exhibited altered gene expression in pathways related to cell cycle, endocytosis, transforming growth factor–β (TGF-β) signaling, and DNA repair (fig. S3, A to D, and table S1). However, stimulation with Tg and PDBu significantly amplified these differences, unmasking additional key trends. Specifically, Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis (GSEA) pathway analysis indicated that Orai1 regulated a broad range of stimulus-responsive programs including pathways related to cell growth and proliferation [including phosphatidylinositol 3-kinase (PI3K)–Akt and mitogen-activated protein kinase (MAPK)], vesicular recycling and cell maintenance (including endocytosis and vesicle recycling), metabolism [such as the tricarboxylic acid (TCA) cycle], and immunity and inflammation (Fig. 2B, fig. S2B, and table S2). Key inflammatory pathways involving interferon-γ (IFN-γ), IFN-α, IL-6, and Janus kinase (JAK)/signal transducer and activator of transcription 3 (STAT3) signaling were significantly down-regulated in Orai1 cKO microglia (Fig. 2, C to E). This deficit was especially evident at the level of individual cytokine-encoding mRNAs, such that the strong induction of classical proinflammatory genes such as Il6, Il1a, Il1b, and Tnf seen in WT cells was significantly blunted in Orai1-deficient microglia (Fig. 2F and fig. S2C). Given the central role of these pathways in immunity and inflammation, their collective down-regulation in Orai1 cKO microglia implies failure to initiate robust cytokine responses upon cell stimulation.

Fig. 2. — WT and Orai1 cKO microglia were exposed to Tg (0.2 μM) and PDBu (50 nM) for 6 hours, and differentially expressed genes (DEGs) were examined by RNA-seq analysis. (A) An MA plot [log-fold change (M) plotted against average expression (A)] showing DEGs between WT and Orai1 cKO microglia. Blue dots indicate genes that are expressed at higher levels in WT than in Orai1 cKO cells; red dots denote indicate genes expressed at lower levels in WT cells; gray dots are genes that are not different between the two genotypes. (B) KEGG pathway enrichment analysis highlighting selected inflammation- and metabolism-related pathways differentially regulated between WT and Orai1 cKO microglia. (C to E) GSEA of Hallmark pathways showing enrichment of genes associated with IFN-α (C), IFN-γ (D), and IL-6 JAK/STAT3 signaling (E) in WT microglia after Tg and PDBu stimulation. P values were adjusted for multiple comparisons using the FDR. (F) Log₂ fold changes of inflammation-related genes compiled from prior publications (87, 88). (G) Hierarchical clustering of genes from the calcium signaling pathway identified by KEGG analysis. The top 50 genes ranked by FDR-adjusted P values, along with *Vdac1* and *Vdac2*, are displayed. The full clustering map is provided in fig. S2. WT group: n = 3 male and 3 female mice treated with Tg and PDBu or DMSO control. Orai1 cKO group: n = 3 male and 2 female mice treated with Tg and PDBu; n = 3 male and 3 female mice treated with DMSO.

Changes in select Ca²⁺-related genes were observed across experimental conditions, particularly in Orai1 expression itself, whose expression increased in WT microglia after Tg and PDBu treatment (Fig. 2G). In Orai1 cKO cells, however, significant reductions were noted in mRNAs for voltage-dependent anion channel 2 (VDAC2), mitochondrial calcium uniporter (MCU), calmodulin (Calm1 and Calm2), sarcoplasmic/endoplasmic reticulum calcium ATPase (SERCA) pumps (Atp2a2 and Atp2a3), the adenylate cyclase isoform Adcy7, and the protein kinase C (PKC) isoform involved in inflammatory gene expression, Prkca. These changes suggest that Orai1 loss dampens expression of Ca²⁺-dependent effectors (Fig. 2G and fig. S2, D and E).

In contrast with enhanced expression of proinflammatory genes seen in WT cells, Orai1 cKO microglia showed significant up-regulation of neurotrophic and anti-inflammatory genes such as Bdnf, Gdnf, Arg1, and Pparg (Fig. 2F and fig. S2C). Pparg in particular encodes a transcription factor associated with anti-inflammatory and metabolic “M2-like” microglial genes, and its elevated expression suggests functional reprogramming in Orai1 cKO microglia (35). Additionally, mitochondrial genes including Arg1 and Ogdhl, whose protein products have anti-inflammatory properties (36, 37), were increased in expression (fig. S2B). These findings suggest that Orai1 cKO microglia exhibit a shift toward a more neurotrophic and protective state.

Orai1 cKO cells also showed down-regulation of genes critical for mitochondrial function and oxidative phosphorylation. These included genes encoding components of complex I (Ndufs8, Ndufb11, Ndufa11, mt-Nd5, and mt-Nd4l), mt-Atp6 (a gene required for ATP synthesis because it encodes a proton transporter), and Mcu (which encodes the mitochondrial uniporter, a multisubunit calcium channel in the mitochondrial inner membrane essential for mitochondrial Ca²⁺ uptake) (fig. S2, B, D, and E) (38). These findings indicate that Orai1 deficiency causes broad reprogramming of metabolic pathways.

Together, these results indicate that Orai1 cKO microglia failed to mount a proper inflammatory response when activated with Tg and PDBu, with decreases seen in both immune responses and metabolic pathways. Instead, Orai1 cKO microglia showed pronounced shifts toward a more neurotrophic and potentially reparative state. This overall trend of reduced proinflammatory responses, combined with increased neurotrophic and anti-inflammatory responses, suggests that Orai1 plays a dual regulatory role in modulating inflammatory pathways. The broad transcriptional shifts observed in Orai1 cKO microglia suggest that Orai1 serves as a molecular switch that tunes inflammatory signaling, mitochondrial function, and cellular homeostasis.

Orai1 stimulates metabolic reprogramming associated with inflammatory responses

A key hallmark of immune cell activation is metabolic reprogramming, which meets the heightened energy demands of producing inflammatory mediators (39–42). Because Ca²⁺ signaling is closely linked to several steps of glycolysis and aerobic respiration (43–45), we next explored the link between Orai1 deficiency and cell metabolism. As described above, our transcriptomic data indicated that Orai1-deficient cells showed down-regulation of several mitochondrial metabolism related genes, including those that encode MCU, VDAC, and key components of the mitochondrial complex I of the electron transport chain (Fig. 2G and fig. S2, D and E). By contrast, Orai1-deficient cells showed upregulated expression of Arg1 and Ogdhl, which encode mitochondrial enzymes linked to anti-inflammatory functions and the latter encoding a key component of the 2-oxoglutarate dehydrogenase multienzyme complex. These results suggest that Orai1 deficiency induces a fundamental shift in microglial metabolism (Fig. 3A).

Fig. 3. — (A) DEGs associated with the TCA cycle and other metabolic pathways in WT and Orai1 cKO microglia after stimulation with TG and PDBu for 6 hours. (B) Metabolic pathways identified by quantitative enrichment analysis using the Global Test (metaboanalyst.ca). Bars above the significance threshold line indicate pathways that are significantly altered between WT and Orai1 cKO microglia (FDR-adjusted P < 0.05) after Tg and PDBu treatment. (C) Hierarchical clustering heatmap of 50 metabolites from WT and Orai1 cKO microglia after TG and PDBu stimulation. Selected metabolites of interest are highlighted in red. (D to F) Relative amount of α-ketoglutarate (D), NAD⁺ (E), and itaconic acid (F) in WT and Orai1 cKO microglia. (G) Relative abundance of metabolites in the ATP cycle and within-sample ATP/adenosine 5′-monophosphate (AMP) and ATP/adenosine 5′-diphosphate (ADP) ratios, normalized to the WT TG and PDBu group. Data are shown as means ± SEM. WT group: n = 3 female and 1 male mice. Orai1 cKO group: n = 2 female and 3 male mice (all treated with Tg and PDBu). P values were calculated using two-tailed unpaired t test. α-KG, α-ketoglutarate; CoA, coenzyme; GAP, guanosine triphosphatase activating protein; GMP, guanosine 5′-monophosphate; UDP, uridine 5′-diphosphate.

To directly investigate this possibility, we performed metabolomics analysis using liquid chromatography–mass spectrometry (LC-MS), comparing over 300 polar metabolites in resting and activated (stimulated with Tg and PDBu) microglia from WT and Orai1 cKO mice. Although there were no baseline differences in unstimulated cells (fig. S4A), Orai1 cKO microglia exhibited lower levels of several key metabolites in the TCA cycle, including α-ketoglutarate and fumaric acid (Fig. 3, B to D, and fig. S4B). However, other intermediates, including nicotinamide adenine dinucleotide (oxidized form) (NAD⁺), were elevated in Orai1 cKO cells (Fig. 3, C and E). A particularly notable change was in itaconic acid, which was significantly elevated in Orai1 cKO microglia compared with WT (Fig. 3F and fig. S4C). Itaconic acid is an immunomodulatory TCA metabolite with anti-inflammatory effects, including suppression of the NOD-, leucine-rich repeat, pyrin domain containing 3 (NLRP3) inflammasome, nuclear factor κB (NF-κB) signaling, succinate dehydrogenase (SDH) inhibition, and macrophage polarization (46–48). The production of itaconic acid occurs through an offshoot of the TCA cycle upstream of α-ketoglutarate, which was decreased in the Orai1 cKO cells (Fig. 3D and fig. S4B). Increased itaconate levels have been linked to the activation of nuclear factor erythroid 2–related factor 2 (NRF2) and activating transcription factor 3, which down-regulate cellular stress responses and promote anti-inflammatory pathways (48).

Additionally, adenosine 5′-monophosphate (AMP) levels were elevated in Orai1 cKO microglia, which is significant because AMP activates AMP-activated protein kinase (AMPK), a known suppressor of inflammation (Fig. 3G and fig. S4D) (49). Likewise, increased NAD⁺ levels are associated with various anti-inflammatory effects, including reduction of oxidative stress and activation of sirtuins, which suppresses proinflammatory cytokine production (50, 51). Together, these findings suggest that the loss of Orai1 shifts microglia toward an anti-inflammatory metabolic profile through alterations in TCA cycle intermediates and downstream signaling pathways.

Orai1 promotes proinflammatory pathways activated by LPS signaling

Given the impact of Orai1 deletion on immune and metabolic gene expression programs, we next examined its role in receptor-mediated microglial activation. The Toll-like receptor 4 (TLR4) agonist lipopolysaccharide (LPS) is commonly used to trigger a proinflammatory microglial state and models CNS inflammation in diseases like Alzheimer’s, Parkinson’s, and chronic pain. (52). TLR4 activation triggers intracellular signaling cascades primarily through myeloid differentiation primary response 88 (MyD88)– and Toll/IL-1 receptor domain–containing adaptor protein inducing interferon β (TRIF)–dependent pathways leading to the production of inflammatory mediators through activation of the transcription factor NF-κB and the MAPK family of kinases (53).

RNA-seq analysis showed that LPS induced a robust inflammatory response in WT microglia marked by up-regulation of IFN-α, IFN-γ, NF-κB, and TNF signaling pathways that are hallmarks of the LPS response in many cells (fig. S5, A to G). Expression of mRNAs encoding proinflammatory cytokines, including Il6, Il1a, Il1b, and TNF, and encoding the anti-inflammatory mediator Il10 were also increased compared with dimethyl sulfoxide (DMSO)–treated controls (fig. S5, A and G). Comparison of WT and Orai1 cKO microglia revealed that Orai1 deletion significantly attenuated these LPS-induced responses (Fig. 4, A and B), and PCA revealed that genotype and treatment, rather than sex, accounted for the majority of variance (fig. S6A), mirroring the patterns seen with Tg and PDBu stimulation.

Among the most significantly down-regulated genes in Orai1 cKO group were proinflammatory cytokines, including Il6, Il1a, Il1b, Ccl3, and Tnf (Fig. 4, A and C; and fig. S6, B and C). Immunomodulatory and cell signaling genes, such as C3, C5ar1, Cd40, Il10, and Ptgs2, were also reduced. KEGG and GSEA pathway analysis showed broad suppression of proinflammatory IFN-α, IFN-γ, and LPS pathway genes in Orai1 cKO microglia (Fig. 4, D to F, and table S3). Further analysis indicated reduced expression of the core elements of the TLR4-MyD88 signaling axis, including Myd88 (which encodes the adapter protein that transduces the TLR4 signaling); Tlr4, Cd14, Tirap, and Ticam1 (which encode the TIR adapter molecule TRAF); and Ticam2 (which encodes the TIR adapter molecule TRAM), indicating that deletion of Orai1 may impair transmission of TLR4 signals to downstream effectors. Orai1 cKO microglia also showed significant down-regulation of Nfkb1, Nfkb2, Rela, Relb, Chuk, and Birc3, which encode core components of the NF-κB complex (Fig. 4G and fig. S6, D and E). The down-regulation of these genes in Orai1 cKO microglia indicates that the deletion of Orai1 induces a global suppression of the NF-κB transcriptional machinery.

In contrast with the down-regulation of proinflammatory genes, Orai1 cKO cells showed increased expression of mRNAs encoding growth factors and genes associated with tissue repair and anti-inflammatory effects, including Bdnf, Gdnf, Arg1, Csf1, and Il4 (Fig. 4C and fig. S6C). This gene expression pattern mirrored the profile seen with Tg and PDBu stimulation (fig. S2C), suggesting a consistent shift in microglial cell state after Orai1 deletion. Bdnf expression through IL-4–mediated Arg1 induction supports neurogenesis in the hippocampus and confers resilience to stress-induced depression (54). Similarly, increased expression of Gdnf and Csf1 implies enhanced trophic support and microglial functions linked to tissue remodeling (55, 56). These findings suggest that the absence of Orai1 promotes a shift in microglia from a classical inflammatory activation phenotype toward an anti-inflammatory, prorepair phenotype.

To assess changes in transcription factor activity between the WT and Orai1 cKO cells, we performed transcription factor motif analysis using the hypergeometric optimization of motif enrichment (HOMER) suite (57). NF-κB and STAT3 binding motifs were significantly enriched in WT cells (Fig. 4, H and I, and fig. S6E), which is notable because Ca²⁺ signaling can activate NF-κB and STAT3 through intermediates such as Ca²⁺-calmodulin–dependent kinases, PKCs, calcineurin, or tyrosine kinases (58, 59). Both transcription factors regulate inflammatory mediators, such as IL-6 and TNF, as well as genes involved in cell growth, all of which showed higher expression in WT compared with Orai1 cKO cells (Fig. 4, H and I). Given their Ca²⁺-dependent regulation and key roles in inflammation and immunity (60, 61), these findings support a key role for Orai1-mediated Ca²⁺ influx in facilitating inflammatory gene expression.

In addition to immune signaling, deletion also affected homeostatic programs related to cell growth, vesicular transport, and protein processing (Fig. 4B) that also occurred in nonstimulated cells (fig. S3, A to D). KEGG analysis identified significant changes in synaptic vesicle cycling (mmu04721), soluble N-ethylmaleimide–sensitive factor attachment protein receptor–mediated vesicular transport (mmu04130), ER protein processing (mmu04141), and cell cycle regulation (mmu04110) (table S3). This pattern suggests that Orai1 broadly regulates microglial homeostasis function in both stimulated and unstimulated cells. Together, these multiple lines of transcriptomics analysis establish Orai1 as a central regulator of microglial inflammatory responses, acting through NF-κB and TLR4 signaling to amplify proinflammatory gene expression in vitro. In the absence of Orai1, inflammatory responses are dampened, but anti-inflammatory and neuroprotective genes are up-regulated, suggesting that Orai1 functions as a molecular switch between pro- and anti-inflammatory microglial states. These findings highlight Orai1’s dual role in both inflammatory and homeostatic processes.

Microglial Orai1 promotes proinflammatory and immune pathways after in vivo LPS challenge

The in vitro results described above show that Orai1 deletion suppresses proinflammatory cytokine production and up-regulates multiple anti-inflammatory responses. To test whether these effects translated into an in vivo setting, we next analyzed gene expression changes in freshly isolated microglia from adult mice administered with LPS to activate microglia in vivo and induce neuroinflammation (52, 62). Systemic LPS causes a strong immune response in peripheral immune cells and causes transient sickness and motivational behavioral changes in both mice and humans, including anhedonia and helplessness (63–65). Although LPS does not cross the blood-brain barrier (66), its effects on the brain are thought to be mediated indirectly through influx of cytokines and activated peripheral immune cells, which ultimately drives neuroinflammation (67, 68).

We administered a single intraperitoneal injection of LPS or saline to WT and Orai1 cKO mice and analyzed gene expression changes by RNA-seq analysis (Fig. 5A) in microglia isolated by fluorescence-activated cell sorting (FACS) (fig. S7) at 6 and 24 hours after LPS administration. PCA revealed stimulus- and time-dependent clustering of microglia, with saline-treated groups clustering together and LPS-treated groups clustering by time point (Fig. 5B). In contrast with the pronounced in vitro differences, transcriptional shifts between WT and Orai1 cKO microglia in vivo were much more modest.

Fig. 5. — (A) Experimental design. Mice received tamoxifen treatment, followed by a 30-day waiting period before a single intraperitoneal injection of LPS (1 mg/kg) or saline. Microglia were isolated by FACS at 6 or 24 hours after LPS administration. RNA-seq analysis was performed on acute freshly, isolated microglia from 10- to 11-week-old mice, 30 to 35 days after the final tamoxifen injection. d, days; h, hours. (B) PCA of RNA-seq data from WT and Orai1 cKO microglia after saline or LPS treatment (6 and 24 hours). Male and female samples are represented by triangles and circles, respectively. (C) River plot summarizing major shifts in gene expression pathways revealed by GSEA across genotypes and time points after LPS challenge. (D to F) Baseline differences between WT and Orai1 cKO microglia after saline injection. (D) GO analysis showing pathways enriched in WT microglia (blue) versus Orai1 cKO microglia (red). (E) GSEA plots showing enrichment of PI3K-Akt-mTOR and TGF-β signaling pathways in Orai1 cKO microglia at baseline. (F) Heatmap highlighting key transcripts that differed significantly between the two genotypes. (G to I) Transcriptional changes at 6 hours after LPS (1 mg/kg) administration in male mice. (G) GSEA GO analysis shows pathways enriched in WT male mice (blue) and Orai1 cKO (red) microglia. (H) GSEA plots showing enrichment of IFN-α and IFN-γ response pathways in WT microglia relative to Orai1 cKO cells. (I) Heatmap of representative transcripts from inflammatory and immune-related pathways. (J to L) Transcriptional changes 24 hours after LPS (1 mg/kg) administration. (J) GSEA GO analysis showing pathways enriched in WT (blue) and Orai1 cKO (red) microglia. (K) GSEA plots showing enrichment of TNF-α signaling through NF-κB in WT cells and mTORC1 signaling in Orai1 cKO cells. (L) Heatmap of selected transcripts from the leading-edge gene sets of significantly enriched pathways. Saline control group: n = 3 WT mice and n = 3 Orai1 cKO mice; 6 hours post–LPS injection group: n = 3 WT mice and n = 3 Orai1 cKO mice; 24 hours post–LPS injection group: n = 4 WT mice and n = 4 Orai1 cKO mice. PLC, phospholipase C; NADH, reduced form of NAD⁺.

To identify coordinated pathway-level changes, we performed GSEA. In saline-treated male mice, WT microglia were enriched for extracellular signal–regulated kinase 1/2 (ERK1/2) signaling, phospholipase C–activating GPCR signaling, calcium-mediated signaling, and tyrosine kinase activity (Fig. 5, C and D), pathways critical for propagating inflammatory and immune receptor signaling. By comparison, Orai1 cKO microglia were enriched for proreparative and protective pathways, including TGF-β signaling, PI3K–Akt–mammalian target of rapamycin (mTOR) signaling, and DNA repair (Fig. 5, C and E). Orai1 cKO microglia exhibited elevated expression of Cdkn1a, encoding the cell cycle regulator p21 (Fig. 5F), suggesting a role for Orai1 in regulating cell cycle progression. These baseline differences indicate that WT microglia are biased toward inflammatory signaling, whereas Orai1 cKO microglia favor reparative programs.

Dynamic changes after LPS injection revealed distinct temporal patterns. At 6 hours, Gene Ontology (GO) analysis identified enrichment in translation, ribosome biogenesis, and RNA stability pathways (Fig. 5G). By contrast, Orai1 cKO microglia at this time point were enriched for pathways related to mitotic spindle assembly, cell migration, and adhesion (Fig. 5, C and G). WT microglia were enriched for several immune pathways, including IFN-α and IFN-γ responses and TNF-α–NF-κB signaling (Fig. 5, C and H), and displayed elevated expression of inflammation markers including Gbp7, which encodes a guanosine triphosphatase induced by IFN signaling and the innate immune sensor Tlr1 (Fig. 5I). These data indicate that WT microglia mount an early, robust proinflammatory response that is muted in Orai1-deficient microglia.

At 24 hours after LPS injection, WT microglia remained enriched for inflammatory pathways, including TNF-α–NF-κB signaling, cytokine and immune receptor activity, ERK1/2 signaling, c-Jun N-terminal kinase (JNK) activity, and vesicular trafficking (Fig. 5, C, J, and K). Moreover, WT microglia showed elevated levels of critical cytokine and immune receptors, including Il6ra, Ccr1, and Tnfrsf1b (Fig. 5L), highlighting broad engagement of signaling cascades driving cytokine production and immune activation. By contrast, Orai1 cKO microglia were enriched for oxidative phosphorylation, unfolded protein binding, DNA repair, mTORC1 signaling, and cell cycle regulation (Fig. 5, C, J, and K). These cell cycle processes included both positive and negative pathways (Fig. 5J), which may be consistent with potential cell cycle arrest or adoption of distinct inflammatory states. Orai1 cKO microglia exhibited delayed enrichment for IFN-α and inflammasome activation at 24 hours (Fig. 5, C and J), suggesting that inflammatory programs are engaged but with slower kinetics relative to WT microglia.

Sex-specific analyses revealed additional differences. In female mice, no significant pathway enrichment differences were observed between WT and Orai1 cKO microglia under baseline (saline) conditions. However, as observed in male microglia after LPS administration, WT microglia were enriched for IFN-α/γ signaling, TNF-α–NF-κB signaling, complement cascade activation, and IFN-β responses (fig. S8, A and B). In contrast, Orai1 cKO microglia showed enrichment for pathways linked to cell cycle regulation, Myc targets, DNA replication, and G₂-M checkpoint control (fig. S8, A and B). Together, the gene expression results from freshly isolated microglia indicate that WT microglia mount a strong and rapid proinflammatory response to systemic LPS challenge, whereas Orai1-deficient microglia display reduced and delayed engagement of inflammatory pathways, instead favoring reparative, cell cycle–associated, and stress protective programs.

Orai1 deletion lowers microglial and astrocyte reactivity and inflammatory cytokines in the brain after peripheral inflammation challenge

The gene expression and metabolic analyses described above described above indicate that Orai1 deletion suppresses proinflammatory cytokine production and inflammatory signaling in microglia while up-regulating multiple anti-inflammatory responses. What are the implications of this regulation for microglial reactivity and CNS inflammation in vivo? We next addressed this question using systemic administration of LPS to induce CNS inflammation. When administered peripherally, LPS triggers a systemic inflammatory cascade that, in turn, leads to the activation of microglia and astrocytes to elevate proinflammatory cytokines in the brain and drive behavioral changes (18, 52, 67). In addition to modeling the neuroinflammatory response of systemic bacterial infections and inflammation, this paradigm is widely used in studies of neuroinflammation associated with neurodegenerative and psychiatric diseases (52, 62). We and others have previously shown that intraperitoneal injections of LPS elevate brain levels of thrombin and stromal cell-derived factor 1α (SDF-1α), two endogenous mediators that stimulate G_q-coupled receptors and that trigger SOCE (18, 69, 70). Systemic LPS also stimulates the hypothalamic-pituitary-adrenal axis to induce a stress response characterized by increases in epinephrine, norepinephrine, prostaglandins, and stress hormones (70, 71). Thus, peripheral LPS initiates brain inflammation not through direct TLR4 signaling in microglia but rather through the induction of multiple soluble mediators that the blood-brain barrier after systemic LPS–induced peripheral inflammation. To investigate the role of microglial Orai1 channels in LPS-induced brain inflammation, we therefore administered LPS or saline intraperitoneally 31 days after the final tamoxifen injection and assessed brain inflammation markers 24 and 48 hours later (Fig. 6A).

Fig. 6. — (A) Experimental timeline. After mice were treated with tamoxifen, there was a 30-day waiting period to permit turnover of peripheral macrophages. LPS (1 mg/kg, intraperitoneal) or saline was administered at time 0, and brains were collected at 24 or 48 hours for immunohistochemistry and ELISA analyses. (B) Representative confocal images of CA1 regions of the hippocampi labeled for microglia (IBA1, red), astrocytes (GFAP, green), and nuclei (DAPI, blue) from WT and Orai1 cKO male mice 24 hours after saline or LPS injections. Scale bars, 100 μm. (C) Quantification of microglial reactivity and proliferation shown as percentages of IBA1 area (left graph) and numbers of IBA1⁺ cells per section (right graph) in the CA1 hippocampus at 24 hours. (D) Quantification of GFAP⁺ areas in the CA1 hippocampi at 24 hours. n = 4 male mice per group and 3 or 4 brain sections per mouse (12 to 16 total data points). Each dot represents one section. (E) Representative images of IBA1 (red), GFAP (green), and DAPI (blue) labeling in the CA1 hippocampi 48 hours after LPS or saline administration. Scale bars, 100 μm. (F) Quantification of percentages of areas of IBA1 (left graph) and numbers of microglia per section (right graph) at the 48-hour time point. (G) Quantification of GFAP⁺ areas at 48 hours. n = 3 or 4 mice per group and 2 to 4 brain sections per mouse. Each dot represents one section. (H and I) ELISA measurements of IL-1β and IL-6 concentrations in hippocampal tissue homogenates 24 hours (H) and 48 hours (I) after LPS or saline administration. Blue dots represent male mice, and pink dots female mice. Sample sizes: 24-hour time point: WT saline group: n = 3 female and 5 male mice; WT LPS group: n = 3 female and 5 male mice; Orai1 cKO saline group: n = 6 female and 4 male mice; Orai1 cKO LPS group: n = 6 female and 4 male mice. Forty-eight-hour time point: WT and LPS saline groups: n = 4 female and 4 male WT mice each; Orai1 cKO and LPS groups: n = 3 female and 4 male Orai1 cKO mice each. Data are shown as means ± SEM. Statistical comparisons were made using two-way ANOVA followed by Tukey’s post hoc test.

We first examined expression of the microglia marker ionized calcium-binding adaptor molecule 1 (IBA1) and the astrocyte marker glial fibrillary acidic protein (GFAP) in the hippocampus, a region where glial responses to peripheral inflammation are well characterized and linked to susceptibility to neuroinflammatory disease (72). Baseline levels of IBA1 and GFAP labeling in the hippocampi [CA1, CA3, and dentate gyrus (DG)] did not significantly differ between WT and Orai1 cKO mice (Fig. 6, B to D; and figs. S9, A to C; S10, A to E; and S11, A to E), indicating that the loss of Orai1 did not meaningfully affect glial reactivity in the absence of external stimulus. However, after peripheral LPS administration, WT mice displayed robust increases in IBA1 immunoreactivity at 24 and 48 hours, reflected by both expanded IBA1-positive areas and increased numbers of IBA1⁺ cells across hippocampal subfields (Fig. 6, B to G; and figs. S9, A to F; S10, A to J; and S11, A to J). Likewise, GFAP expression was also significantly elevated in CA1, CA3, and DG in both males and females (Fig. 6, B to G; and figs. S9, A to F; S10, A to J; and S11, A to J). Alongside these molecular changes, microglia exhibited pronounced morphological transformation: Cells shifted from a resting, highly ramified form with small soma and long, thin processes to an activated morphology characterized by enlarged, amoeboid-like cell bodies and markedly shortened, thickened processes (fig. S12, A to D). These changes are consistent with hallmarks of glial reactivity after systemic administration of LPS (3). In contrast, female WT mice displayed transient responses, with IBA1 and GFAP expression returning to baseline by 48 hours, whereas male WT mice maintained elevated glial activation at this later time point (figs. S9, A to F; and S11, A to J).

In contrast, male Orai1 cKO mice failed to exhibit LPS-induced increases in IBA1 or GFAP labeling at either 24 or 48 hours after LPS administration (Fig. 6, B to G; and fig. S10, A to J). These mice also lacked the characteristic LPS-evoked rise in microglial cell numbers and showed little to no morphological shift from ramified to hypertrophic, activated forms (fig. S12, A to B). Likewise, female Orai1 cKO mice displayed significant attenuation of the LPS-evoked increases in IBA1 and GFAP labelling at 24 hours (figs. S9, A to C; and S11, A to E). Together, these findings demonstrate that conditional deletion of Orai1 in microglia dampens both microglial and astrocytic reactivity to systemic inflammatory challenge, and the decrease in both microglial and astrocyte reactivity is consistent with emerging evidence that microglia-astrocyte signaling interactions amplify CNS inflammatory cascades (73, 74).

IBA1 and GFAP are generalized markers for activated microglia and astrocytes and do not distinguish between inflammatory from neuroprotective responses. Hence, we also assessed brain levels of two key proinflammatory cytokines by enzyme-linked immunosorbent assay (ELISA). Levels of IL-6 and IL-1β in homogenized hippocampal tissue were significantly elevated in response to peripheral LPS administration in WT mice (Fig. 6, H and I). These cytokines elevate neuroinflammation in vivo in various disease states and induce alterations in neuronal, synaptic, and cognitive functions (68, 75). Both male and female WT mice showed comparable increases in cytokine levels compared with saline-injected controls (Fig. 6, H and I). However, microglial Orai1 cKO mice showed significant decreases in the levels of IL-6 and IL-1β both 24 and 48 hours after LPS administration compared with WT mice, and the LPS-evoked increases in these inflammatory cytokines were blunted in microglial Orai1 cKO mice (Fig. 6, H and I). Together with the previous results showing mitigation of glial reactivity markers, these results argue that microglia play a key role in amplifying (enhancing) brain levels of inflammatory cytokines in response to systemic inflammation and that this regulation of inflammatory cytokine levels in the brain is controlled by Orai1 signaling in microglia.

Deletion of Orai1 in microglia mitigates inflammation-induced depression-like behaviors

In both humans and animal models, the systemic inflammation ensuing from peripheral LPS administration induces changes in motivational behaviors that mirror affective phenotypes in humans, such as anhedonia (loss of reward-seeking behavior) and helplessness (avoidance behaviors) that can last for days (62–65). Mice typically show signs of sickness as measured by murine sepsis scores (MSSs), which are accompanied by diminished locomotion in the open-field test (OFT) (18, 62). These symptoms peak ~6 hours after LPS injection and subside ~24 hours after LPS injection, after which tests for motivational behaviors can be performed (18). We examined reward-seeking behavior through the sucrose preference test (SPT), a reward-based test that exploits the innate interest of mice for sweet foods, and avoidance behaviors via the forced swim test (FST) and tail suspension test (TST) (Fig. 7, A and B). These latter tests measure escape-related mobility displayed by mice as metrics for behavioral despair. All three tests are widely used for screening efficacy of clinically used antidepressants with LPS to induce depression-like behaviors (64, 76, 77).

Six hours after LPS administration, both WT and Orai1 cKO mice showed signs of sickness as assessed by MSSs and reduced locomotion (Fig. 7, C and D; and fig. S13, A and B). These symptoms subsided 24 hours after LPS administration (Fig. 7, C and D; and fig. S13, A and B). Male WT mice administered with a single injection of LPS displayed significant loss of interest in reward (sugar water) and reduced avoidance behaviors (increased immobility) in the FST and TST compared with saline-treated controls (Fig. 7, E to G). These findings align with previous studies indicating that LPS induces motivational behaviors in male mice characterized by anhedonia and helplessness (18, 63). In the absence of LPS administration, conditional deletion of microglial Orai1 did not affect sucrose preference or immobility times in the TST and FST tests at baseline. However, in contrast with WT mice, microglial Orai1 cKO mice maintained their preference for sweetened water in the sucrose water test even after LPS administration compared with saline-injected cKO mice (Fig. 7E). Moreover, male Orai1 cKO mice did not show increased immobility times in the FST and TST after LPS administration compared to saline-injected KO mice, in contrast with the increased immobility seen in WT mice that received LPS (Fig. 7, F and G). These results indicate that interruption of microglial Orai1 Ca²⁺ signaling prevents the emergence of maladaptive, depression-like behaviors, including anhedonia and behavioral despair, caused by systemic LPS administration.

In contrast with male mice, female WT mice failed to exhibit the LPS-evoked changes in reward and avoidance behaviors in all three tests (fig. S13, C to E), which is consistent with previous literature showing that female mice are protected from induction of depression-like behaviors after LPS challenge (18, 78–80). In the absence of an LPS-stimulus evoked response in the WT mice, there was also no difference in the reward or avoidance behaviors between WT and Orai1 cKO mice. Our own data indicate that, although WT female mice exposed to LPS do show elevated levels of the neuroinflammatory markers GFAP and IBA1, deletion of Orai1 dampened this up-regulation (fig. S9, A to C), arguing that the lack of behavioral phenotype is not due to the absence of an inflammatory response in female mice to LPS but due to other protective factors. Brain-derived nerve factor (BDNF) protects female mice from depression-like behaviors after LPS exposure (81), which may explain the lack of behavioral effect seen here in Orai1 cKO female mice.

These changes in reward-seeking and avoidance behaviors in the Orai1 cKO mice were not due to generalized alterations in mobility or motivational behavior at baseline (Fig. 7, D and H). In the OFT, WT and Orai1 cKO mice showed comparable levels of distance traveled. However, male Orai1 cKO mice spent slightly more time in the middle relative to WT mice (Fig. 7, D and H). This apparent mitigation of anxiety-like behavior was not detected in the elevated zero-maze test, which is considered to be a more stringent test for anxiety-like behaviors (Fig. 7I). Neither genotype nor LPS elicited changes in the OFT zone and in the zero-maze test in female mice (fig. S13, F and G). Together, these results indicate that deletion of microglial Orai1 confers relatively specific protection against behavioral depression in male mice without causing global impairments in locomotion, anxiety, or exploratory behaviors.

DISCUSSION

In this study, we show that Orai1 Ca²⁺ channels are central regulators of microglial reactivity, microglia-driven neuroinflammation, and the development of depression-like behaviors after systemic inflammatory challenge. Microglia are highly plastic and reversibly interconvert between different cell states through signaling networks (7). Our findings indicate that Orai1 channels orchestrate this plasticity by controlling gene expression programs governing inflammation, metabolism, and proliferation. Deletion of Orai1 led to metabolic reprogramming, reduced neuroinflammatory markers in the brain, and alleviated the development of depression-like behaviors in response to neuroinflammation (Fig. 8). These results highlight microglial Orai1 channels as promising targets for modulating brain inflammation and affective mood disorders.

Fig. 8. — Activation of Orai1-mediated SOCE stimulates microglial metabolism, transcription of proinflammatory genes, and release of inflammatory mediators after acute peripheral inflammation, leading to neuroinflammation and depression-like behaviors. In contrast, genetic deletion of Orai1 attenuates SOCE, suppresses proinflammatory signaling, shifts microglia toward a proreparative transcriptional state, and protects mice against LPS-induced neuroinflammation and behavioral alterations. Created in BioRender. α-KG, α-ketoglutarate; CoA, coenzyme A.

Transcriptomic (RNA-seq) analysis of in vitro–stimulated microglia revealed profound differences in inflammatory gene expression between WT and Orai1 cKO microglia. Orai1 deletion down-regulated genes encoding classical inflammatory mediators, such as IL-6, TNF, IL-1β, IL-1α, C3, and Nos2, as well as chemokines such as CCl3 and CCl22. Genes encoding proinflammatory IFNs (IFN-γ, IFN-α, and IFN regulatory factor 1) were similarly down-regulated. Because these genes are enriched for NF-κB and STAT3 binding sites in their promoters, we believe that these transcriptional changes likely reflect suppressed NF-κB and STAT3 signaling (Fig. 4, H and I). Additionally, Orai1 deletion led to reduction in the expression of several Ca²⁺-related transporters, effectors, and channels, including the genes encoding calmodulin isoforms (Calm1 and Calm2), the mitochondrial calcium uniporter (Mcu), SERCA pumps (Atp2a2 and Atpa3), and the mitochondrial metabolite transporter (Vdac2). Together, these findings indicate that Orai1-mediated Ca²⁺ signaling is a key driver of microglial reactivity and proinflammatory signaling.

In contrast, Orai1 cKO microglia exhibited enhanced expression of multiple anti-inflammatory and neuroprotective markers. Expression of genes encoding BDNF, GDNF, and metabolomic enzymes including arginase 1 (ARG1) and oxoglutarate dehydrogenase-like (OGDHL) were all elevated. BDNF and GDNF are key growth factors that promote growth and maintenance of neurons, particularly in the hippocampus and cortex and have neuroprotective and anti-apoptotic roles (82, 83). ARG1, an enzyme in the urea cycle, suppresses inflammatory responses, promotes tissue repair, and supports inflammation resolution in macrophages (36). OGDHL, a mitochondrial enzyme involved in the TCA cycle and glutamate-glutamine metabolism, modulates α-ketoglutarate levels and has strong anti-inflammatory and neuroprotective effects due to suppression of PI3K-Akt and NF-κB signaling to reduce cell growth and inflammation (37, 84). Together, these transcriptional shifts suggest that Orai1 deletion promotes a transition from a proinflammatory to an anti-inflammatory microglial phenotype.

Further support for this phenotypic shift comes from metabolomics data, which showed increases in the anti-inflammatory metabolite itaconic acid, as well as elevated AMP and NAD⁺ levels in Orai1 cKO microglia. Itaconic acid exerts anti-inflammatory effects by inhibiting SDH and the production of reactive oxygen species (46, 47). This inhibition activates the NRF2 pathway, which promotes the expression of antioxidant and anti-inflammatory genes, and also inhibits NF-κB signaling to diminish inflammation (46–48). Similarly, elevated NAD⁺ levels are associated with suppressed inflammation in various cell types. Increased AMP levels can activate AMPK, a key regulator of energy homeostasis that also suppresses inflammation and metabolic dysfunction (49). Collectively, these findings indicate that blocking Orai1 signaling reduces inflammation not only by dampening proinflammatory pathways but also by activating protective, anti-inflammatory programs.

Peripheral inflammation does not stay “peripheral.” Through cytokines, neural signals, and vascular activation, peripheral inflammation activates glia in the brain, and this glial response is a critical step linking systemic immune responses to CNS function (4, 5, 68). Glial neuroimmune responses can be protective in the short term but detrimental if chronic or exaggerated, contributing to cognitive, affective, and degenerative pathologies (2, 6). Here, we found that, after intraperitoneal LPS challenge, Orai1 cKO mice were protected against microglial and astrocyte reactivity in the hippocampus, as evidenced by reduced LPS-induced IBA1 and GFAP immunoreactivity (Fig. 6, B to F). Microglial Orai1 deletion also significantly diminished hippocampal protein levels of IL-1β and IL-6 after peripheral inflammatory challenge (Fig. 6, H and I). Analysis of freshly isolated microglia after systemic LPS challenge confirmed that Orai1 drove rapid proinflammatory transcriptional responses in vivo. At both 6 and 24 hours post-LPS, WT microglia showed enrichment for cytokine, IFN, and NF-κB signaling pathways; sustained activation of TNF-α–NF-κB, ERK, and JNK cascades; and up-regulation of immune receptors. By contrast, Orai1-deficient microglia showed either reduced or delayed engagement of these programs, instead favoring pathways associated with DNA repair, cell cycle regulation, oxidative phosphorylation, and mTOR signaling, all hallmarks of reparative and protective cellular states. (Fig. 5, D to L; and fig. S8, A and B). Collectively, these in vivo results demonstrate that Orai1 promotes proinflammatory microglial reactivity, whereas its loss biases microglia toward protective and homeostatic responses.

Behavioral tests further demonstrated the functional importance of Orai1-mediated microglial activation. Male Orai1 cKO mice were protected against maladaptive, depression-like motivational changes after peripheral LPS challenge. Specifically, reward-seeking behavior (as assessed by the SPT) and active escape-related coping strategies (as assessed by the FST and TST) remained intact in Orai1 cKO mice, contrasting with the severe deficits observed in WT littermates. These findings indicate that Orai1-driven microglial reactivity and proinflammatory responses play a critical role in the neuroimmune communication linking systemic inflammation to motivational dysfunctions. However, LPS-induced behavioral changes were absent in female WT mice, which complicates addressing Orai1’s role in regulating these behaviors in female mice. This sex difference is believed to be due to BDNF’s protective effects in female rodents (79). Heterozygous deletion of BNDF in female mice is sufficient to reveal the maladaptive behavioral deficits in responses to LPS (81). Although further behavioral assays are needed to fully understand Orai1’s role in females, our data showed that microglial Orai1 deletion also reduced inflammatory markers in female mice (figs. S9, A to J; and S11, A to E), highlighting Orai1’s therapeutic potential across sexes.

Together, these findings indicate that Orai1 is a key regulator of microglial cell states, coordinating the balance between reactive, proinflammatory, and protective, homeostatic functions. Through dual suppression of inflammatory cascades and activation of anti-inflammatory programs Orai1 deletion fundamentally reprograms microglial responses to inflammatory challenge (Fig. 8). These findings identify Orai1 as a potential target for therapeutic intervention in brain disorders, such as depression, neurodegenerative diseases, and chronic pain, that are characterized by microglial activation and chronic neuroinflammation.

MATERIALS AND METHODS

Mice

All mice in this study maintained in accordance with institutional guidelines and the Guide for the Care and Use of Laboratory Animals. All procedures were approved by Northwestern University’s Institutional Animal Care and Use Committee. Male and female C57Bl/6 mice were used in approximately equal numbers. Mice were group housed in a sterile-ventilated facility under standard housing condition (12-hour light/12-hour dark cycle with lights on at 7:00 a.m., temperature between 20° and 22°C) with ad libitum access to water and food.

The generation of Orai1^fl/fl mice has been previously described previously (69). To achieve tissue-specific deletion of Orai1 in microglia, Orai1^fl/fl mice were bred with Cx3CR1-Cre/ERT2 mice to generate Orai1^{fl/fl Cx3CR1-cre/ERT2} mice as described previously (22). For in vitro Cre induction, microglia cultures were treated with 1 μM 4-hydroxytamoxifen (H7904, Sigma-Aldrich) for 12 days. The compound was removed on day 13, when microglia were isolated from mixed glial cultures.

For in vivo Cre induction, Orai1^fl/fl and Orai1^{fl/fl Cx3CR1-Cre/ERT2} received intraperitoneal injections of tamoxifen (75 mg/kg; T5648, Sigma-Aldrich, USA) once daily for 5 consecutive days at 5 to 6 weeks of age. Mice were then allowed to recover for 30 to 35 days after the final tamoxifen injection before their use for behavior, immunohistochemistry, and tissue homogenate experiments.

Primary neonate microglia cultures

Primary microglia were isolated from mixed glial cultures prepared from hippocampal tissue of P2 to P5 mice, as described previously (18, 22). Hippocampi were dissected in ice-cold Hanks’ balanced salt solution (HBSS; 14175–095, Gibco) under a dissection microscope. Tissue was enzymatically digested with 0.25% trypsin (15090046, Invitrogen) and deoxyribonuclease I (DNase I; 1 mg/ml; 1014159001, Roche) for 18 min at 37°C, with gentle inversion in a water bath every 5 min. Digestion was stopped by washing twice with HBSS, followed by mechanical trituration in culture medium consisting of Dulbecco’s modified Eagle’s medium (10–013-CM, Corning) supplemented with 10% fetal bovine serum (FBS; 26140–079, Gibco) and 1% penicillin-streptomycin (15140122, Gibco). Cells suspensions were passed through a 70-μm cell strainer and plated on poly-L-lysine–coated (P4707, Sigma-Aldrich, USA) 25-mm² tissue culture flasks with 10 ml of culture medium. On day 2, the medium was completely exchanged, and 4-hydroxytamoxifen (1 μM) was added to the culture medium. Half-medium exchanges were performed every 3 to 4 days for 12 to 14 days.

To isolate microglia, flasks were shaken at 200 rpm for 1 hour at 37°C to detach cells from the astrocyte layer. Suspended cells and media were collected and centrifuged at 300g for 5 min at 4°C. The supernatant was aspirated, and pellets were resuspended and plated on poly-L-lysine dishes in a serum-free medium for 2 days before functional assays. The serum-free medium contained N-acetyl cysteine (5 μg/ml, Sigma-Aldrich), apo-transferrin (100 μg/ml, Sigma-Aldrich), sodium selenite (100 ng/ml, Sigma-Aldrich), oleic acid (0.1 μg/ml, Caymen Chemical), gondoic acid (0.001 μg/ml, Caymen Chemical), 1× GlutaMAX (Gibco), 1× penicillin-streptomycin (Gibco), TGF-β2 (2 ng/ml, PreproTech), colony-stimulating factor 1 (CSF1, 10 ng/ml, BioLegend), hepran sulfate (1 μg/ml, Galen Laboratory Supplies), and ovine wool cholesterol (1.5 μg/ml, Avanti Polar Lipids) (85). Microglia were plated at 40,000 cells per well in a 24-well plate for RNA-seq and 60,000 cells per well for metabolomics analysis.

Isolation of microglia from adult mice

Adult Orai1^fl/fl and Orai1^{fl/fl Cx3CR1Cre/ERT2} mice (10 to 12 weeks of age; 30 to 35 days after the last tamoxifen injection) were perfused with 20 ml of ice cold HBSS to remove blood from the brain. Brains were dissected out and the cerebellum removed. The brain tissue was minced in cold HBSS, transferred to a 15-ml conical tube, and pelleted. The supernatant was aspirated, and the tissue was resuspended in 500 μl of HBSS and 500 μl of enzymatic digestion buffer containing papain (NC9199962, Worthington Biochemical Corporation) prepared in 4.5 ml of HBSS (14025092, Gibco) and 500 μl of DNase I (10 mg/ml) (1014159001, Roche). Samples were incubated at 37°C for 20 min with gentle agitation by tapping the tubes on the lab bench every 5 min. Tissue was then dissociated by trituration with P1000 and P200 pipettes, and digestion was stopped by adding 1% bovine serum albumin (BSA; A3294, Sigma-Aldrich). Cells were centrifuged at 400g for 10 min at 4°C and resuspended in 40% Percoll (17–0891-09, GE Healthcare). After centrifugation at 500g for 30 min, the myelin layer was removed, and cells were washed twice with phosphate-buffered saline (PBS). The pellet was resuspended in 100 μl of magnetic-activated cell sorting (MACS) buffer (130–091-221, Miltenyi Biotec) and incubated with 20 μl of CD11b magnetic beads (130–093-636, Miltenyi Biotec) for 15 min at 4°C. The incubation was stopped by adding 1 ml of MACS buffer, followed by centrifugation at 300g for 5 min at 4°C. Cells were resuspended in 500 μl of MACS buffer, passed through a magnetic column, and washed three times before eluting the microglia fraction. Microglia were washed with PBS and divided for subsequent reverse transcription quantitative polymerase chain reaction (RT-qPCR) or Fura-2-AM Ca²⁺ imaging studies. For RT-qPCR analysis, ~100,000 cells were collected for RNA extraction.

FACS isolation of adult microglia for bulk RNA-seq

After the PBS wash step described above, cells were resuspended in 50 μl of Fc block (1:50 dilution) (BD Pharmingen, 553142; clone 2.4G2) in MACS buffer and incubated for 5 min on ice. An antibody cocktail (50 μl) (table S4) was added to the cell suspension and mixed with gentle pipetting. After a 30-min incubation at 4°C in the dark, 900 μl of MACS buffer was added, and cells were pelleted at 400g for 5 min at 4°C. The pellet was resuspended in 1 ml of MACS buffer, filtered through 70-μm strainer, and stained with SYTOX (1 μl/ml). Microglia were sorted as nondebris, live (SYTOX negative), CD3e negative, CD45 int, CD64 positive, CD11b positive, Ly6C negative, Ly6G negative, and CD206 negative (table S4). Sorted cells were collected directly into RLT buffer mixed with β-mercaptoethanol (74004, QIAGEN) at 7°C with occasional mixing. Up to 50,000 cells were collected per tube, which was mixed vigorously and placed on ice until the end of sorting all samples. All samples were then stored at −80°C freezer until RNA isolation.

Wide-field Fura-2 Ca²⁺ imaging

Glass bottom 35-mm dishes were coated with either collagen IV (2 μg/ml; 354233, Corning) for 2 hours at 37°C or poly-L-lysine overnight. Microglia were seeded at ~20,000 cells per dish in serum-free culture medium. Ca²⁺ imaging on neonatal microglial cultures was performed on microglia isolated from P2 to P5 mice.

Cells were incubated with 2 μM Fura-2-AM in serum-free medium supplemented with and 5% FBS for 30 min at 37°C. After 30 min, the medium was washed, and cells were incubated for 10 to 15 min at room temperature before imaging. Single-cell [Ca²⁺]_i measurements were performed as described previously (18, 22). For image acquisition, dishes were mounted on an Olympus IX71 inverted microscope, and images were collected every 6 s using SlideBook software (Denver, CO) at excitation wavelengths of 340 and 380 nm with emission recorded at 510 nm. The standard Ringer’s solution contained 155 mM NaCl, 4.5 mM KCl, 10 mM d-glucose, 5 mM Hepes, 1 mM MgCl₂, and 2 mM CaCl₂ (pH 7.4, adjusted with 1 M NaOH). Ca²⁺-free Ringer’s solution was identical except that it contained 1 mM EGTA and 3 mM MgCl₂ with no added CaCl₂. Tg was dissolved in DMSO and used at 1 to 2 μM.

For data analysis, regions of interest (ROIs) were drawn around single cells, background was subtracted, and F₃₄₀/F₃₈₀ ratios were calculated for each time point. [Ca²⁺]_i was estimated using the standard equation: [Ca²⁺]_i = βK_d (R − R_min)/(R_max − R), where R is the F₃₄₀/F₃₈₀ fluorescence ratio, R_min and R_max were determined from in vitro calibration of Fura-2 pentapotassium salt, K_d is the apparent dissociation constant of Fura-2 for Ca²⁺ (135 nM), and β was derived from the F₃₄₀/F₃₈₀ ratio at 380 nm. Measurement of β, R_min, and R_max obtained in solution were adjusted for in situ values by multiplying β by 1.4, R_min by 0.88, and R_max by 0.66. SOCE was quantified for each cell as the rate of increase in [Ca²⁺]i (Δ[Ca²⁺]i/Δt), calculated from the slope of a line fitted to three consecutive data points (12 s) immediately after the readdition of 2 mM extracellular Ca²⁺.

Bulk RNA-seq analysis

Microglia were isolated as described above. RNA isolation was performed either with the QIAGEN RNeasy Plus Mini kit (74134, QIAGEN) (for cultured microglia) or the QIAGEN RNeasy Micro kit (74004, QIAGEN) (for freshly isolated microglia from adult mice) according to the manufacturer’s protocols. RNA quality and quantity were assessed using TapeStation 4200 RNA tapes (Agilent). For the cultured microglia experiments, RNA-seq libraries were prepared from 100 ng of total RNA using the NEBNext Ultra DNA Library Prep Kit for Illumina (NEB E7370L). Library QC was then performed using TapeStation 4200 DNA tapes (Agilent). For the RNA-seq experiments using freshly isolated microglia, RNA quality and quantity were assessed using TapeStation 4200 High-Sensitivity RNA tapes (Agilent), and RNA-seq libraries were prepared from 0.97 ng of total RNA using the SMARTer Stranded Total RNA-seq kit v2 (Takara Bio). For analysis of allele status, libraries were prepared using the NEBNext Ultra DNA Library Prep kit for Illumina (NEB E7370L). Library quality control was then performed using TapeStation 4200 High-Sensitivity DNA tapes (Agilent). Dual-indexed libraries were pooled and sequenced on a NextSeq2000 instrument (Illumina) for 100 cycles, single-end, to an average sequencing depth of 11.63 million reads per sample.

FASTQ files were generated using bcl-convert 4.0.3 using default parameters. To facilitate reproducible analysis, samples were processed using the publicly available nf-core/RNA-seq pipeline version 3.16.1 implemented in Nextflow 22.10.1 using Singularity 3.8.1 with the minimal command nextflow run nf-core/rnaseq \ -r ‘3.16.1’ \ -profile nu_genomics \--additional_fasta ‘transgenes.fa’ \ --star_index false \ --genome ‘GRCm38’. Briefly, lane-level reads were trimmed using trimGalore! 0.6.7 and aligned to the hybrid genome described above using STAR 2.7.10a. Gene-level assignment was then performed using salmon 1.10.1.

Differential gene expression analysis

All analysis was performed using custom scripts in R version 4.3.0 using the DESeq2 version 1.42.1 framework. A “local” model of gene dispersion was used as this better fit dispersion trends without obvious overfitting, and pairwise comparisons were performed using Wald tests on a combined factor of treatment and genotype. Alpha was set at 0.05 for all DEA. Otherwise, default settings were used. High-level analysis was performed using custom scripts available in the nu-pulmonary/utils GitHub repository.

k-means clustering

The k_means_figure function from nu-pulmonary/utils was used for k-means clustering. Briefly, variable genes were identified using a likelihood-ratio test with local estimates of gene dispersion in DESeq2 with diagnosis as the full model as well as a reduced model corresponding to intercept alone (~1). Genes with q ≥ 0.05 were discarded. Extant genes were then clustered using the Hartigan-Wong method with 25 random sets and a maximum of 1000 iterations using the kmeans function in R stats 4.3.0. Samples were then clustered using Ward’s method and plotted using pheatmap version 1.0.12. GO term enrichment was then determined using Fisher’s exact test (classic mode) in topGO version 2.54.0, with org.Mm.eg.db version 3.18.0 as a reference.

GSEA and HOMER analysis

GSEA was performed using the fgsea 1.28.0 package was used. “Hallmark” and “Gene Ontology” gene set lists were downloaded from the Molecular Signatures Database 7.5.1 at www.gsea-msigdb.org/gsea/downloads.jsp. Additional gene sets were included for disease-associated microglia, integrated stress response, and LPS response (GO:0071222). Enrichment analysis was performed for all gene sets simultaneously using the “fgseaMultilevel” method using gene-level Wald statistics as rankings and default parameters.

To determine potential common transcription factor regulators of gene hits, significantly differentially expressed genes [false discovery rate (FDR) < 0.05, Wald test, DESeq2] were exported to a text file as entrezgene IDs. The “findMotifs.pl” function from HOMER 4.10 was then run using default parameters with the minimal command findMotifs.pl “gene_hits.txt” \ mouse. For motif-based transcription factor prediction, “homerResults” were used. For known transcription factor regulator enrichment, “knownResults” were used.

RNA-seq statistical analysis

For all analyses, normality was first determined using the Shapiro-Wilk test and visual inspection of histograms. In cases in which distributions were clearly nonnormal, nonparametric statistics were used. All analysis was performed using custom scripts in R 4.3.0, all of which are publicly available on GitHub at nu-pulmonary/utils. Plotting was performed using ggplot2 3.5.1 unless otherwise noted. Comparisons for these figures were added using ggsignif 0.6.4. Heatmaps were generated using pheatmap 1.0.12 using Euclidean distance as the distance metric and the Ward D2 clustering method.

Metabolomics

Primary cultured microglia were stimulated for 6 hours with either 0.2 μM Tg and 50 nM PDBu or vehicle control (DMSO). After stimulation, cells were rinsed twice with ice-cold saline and scraped into chilled 80% acetonitrile (−80°C) on dry ice. Lysates were snap frozen at −80°C for 5 min and vortexed at room temperature for 60 s, and this cycle was repeated three times. Samples were stored overnight at −80°C and then centrifuged at 20,000g for 15 min at 4°C. The supernatant was collected, and the extraction solvent was evaporated to dryness using a SpeedVac. The remaining protein pellet was dissolved in 8 M urea, diluted, and analyzed by BSA assay for normalization to protein content. Dried metabolite extracts were reconstituted in 60% acetonitrile, vortexed for 30 s, and centrifuged at 20,000g for 30 min at 4°C. The resulting supernatant was collected for LC-MS analysis.

Polar metabolites were analyzed by high-performance liquid chromatography coupled with high-resolution mass spectrometry (HPLC-MS/MS). The system consisted of a Thermo Q-Exactive mass spectrometer with electrospray ionization (ESI) interfaced to an Ultimate 3000 HPLC (Thermo Fisher Scientific) equipped with a binary pump, degasser, and autosampler. Chromatographic separation was performed on an Xbridge Amide column (3.0 mm by 100 mm, 3.5-μm particle size; Waters). The mobile phase consisted of: (A) 95% water, 5% acetonitrile, 10 mM ammonium hydroxide, and 10 mM ammonium acetate (pH 9.0); and (B) 100% acetonitrile. The gradient was: 0 min, 15% A; 2.5 min, 64% A; 12.4 min, 40% A; 12.5 min, 30% A; 12.5 to 14 min, 30% A; and 14 to 21 min, 15% A, at a flow rate of 150 μl/min.

Mass spectrometry was performed with the after parameters: ESI capillary temperature, 275°C; sheath gas, 35 arbitrary units (a.u.); auxiliary gas, 5 a.u.; and spray voltage, 4.0 kV. Data were collected in positive/negative polarity switching mode over a mass/charge ratio (m/z) range of 60 to 900. MS1 spectra were acquired at a resolution of 70,000 with an automatic gain control target of 1 × 10⁶ and maximum injection time of 200 ms. Targeted ions were fragmented in the higher energy collisional dissociation cell at 30% normalized collision energy, and MS2 spectra were collected at a resolution of 17,500. Metabolites were identified by matching m/z values, retention times to analytical standards, and/or MS2 fragmentation patterns. Data acquisition and analysis were performed using Xcalibur 4.1 and TraceFinder 4.1 (Thermo Fisher Scientific).

Two-step RT-qPCR analysis

Total RNA was extracted from mouse microglia cultures using the RNeasy Plus Mini Kit (74134, QIAGEN). cDNA was synthesized using the High-Capacity cDNA Reverse Transcription Kit (4368814, Applied Biosystems, Thermo Fisher Scientific). qPCR reactions were performed with PowerUp SYBR Green Master Mix (A25741, Applied Biosystems, Thermo Fisher Scientific) following the manufacturer’s instructions. Each reaction contained 8 ng of cDNA and primers at a final concentration of 500 nM. Amplification was performed on a CFX Connect Real-Time System (Bio-Rad), and data were analyzed with Bio-Rad CFX Maestro software. Sequences of primers are as follows: Orai1 forward, 5′- AGACTGCCTGATCGGATGGC-3′; Orai1 reverse, 5′- TTGTCCCCGAGCCATTTCCT-3′; Gapdh forward, 5′-AGGTCGGTGTGAACGGATTTG-3′; GAPDH reverse, 5′-ATGTA GACCATGTAGTTGAGGTCA-3′; Orai2 forward, 5′-GCAGCTACCTGGAACTCGTC-3′; Orai2 reverse, 5′-GTTGTGGATGTTGCTCCCG-3′; Orai3 forward, 5′-CAGTCAGCACTCTCTGCGG-3′; Orai3 reverse, 5′-TGGCCACCATGGCGAAG-3′; STIM1 forward, 5′-ATTCGGCAAAACTCTGCTTC-3′; Stim1 reverse, 5′-GGCCAGAGTCTCAGCCATAG-3′; STIM2 forward, 5′-TCGAAGTGGACGAGAGTGATG-3′; Stim2 reverse; and 5′-TTTCCACTGTTTCCACAAATCC-3′.

Administration of LPS to adult mice

Thirty to 35 days after the final tamoxifen injection, adult mice (10 to 12 weeks old) received an intraperitoneal injection of either sterile PBS or LPS (Escherichia coli O111:B4, 1 mg/kg; L4391, Sigma-Aldrich) dissolved in PBS. Mice were randomly assigned to LPS or saline groups and monitored for 6 hours postinjection for signs of distress. Behavioral testing began 24 hours after treatment. Brain tissue was collected at 24 and 48 hours after injection for immunohistochemistry and ELISA analysis.

Immunohistochemistry

Mice were anesthetized with isoflurane and perfused intracardially with cold PBS to clear blood from the brain. Brains were removed, fixed overnight at 4°C in 10% formalin and then transferred to 30% sucrose with 0.1% sodium azide for long-term storage. Brains were sectioned at 40 μm and stored free-floating at −20°C in cryoprotectant solution (30% sucrose and 30% ethylene glycol in 0.1 M phosphate buffer).

For staining, sections were washed with tris-buffered saline (TBS) to remove cryoprotectant and blocked for 2 hours at room temperature in 10% goat serum with 0.3% Triton X-100 in TBS. Sections were then incubated for 48 hours at 4°C with primary antibodies—rabbit anti-IBA1 (1:1000; 019–19741, Wako Chemicals) and mouse anti-GFAP (1:500; 14–9892-82, Thermo Fisher Scientific)—diluted in wash buffer (1% BSA and 0.25% Triton X-100 in TBS). After washes, sections were incubated for 2 hours at room temperature with secondary antibodies (goat anti-rabbit Alexa 594, 1:1000, Thermo Fisher Scientific; and goat anti-mouse Alexa 488, 1:1000, Abcam). Nuclei were counterstained with 4′,6-diamidino-2-phenylindole (DAPI), and sections were mounted on charged slides with mounting medium (H-1200, Vector Labs).

Confocal images of the hippocampus (CA1, CA3, and DG) were acquired using a Nikon A1R confocal microscope with a 25× Nikon CF1 APO LWD objective. Imaging parameters, z-stack step size, and projection settings were kept constant across groups (time point, genotype, treatment, sex, and region). Z-stacks were flattened to maximum intensity projections and analyzed in Nikon Elements software. For percent area quantification, the ROI was outlined, background was subtracted, and a fluorescence threshold was applied; percent area was calculated as the labeled area divided by total ROI. Microglia counts were performed in CA1 by a blinded experimenter using IBA1⁺ and DAPI⁺ labeling. For morphological analysis, image brightness was uniformly adjusted across all samples. An ROI containing representative microglia was selected, and identical threshold parameters were applied to generate binary images. Background elements, including cells or processes not belonging to the cell of interest, were manually removed.

ELISA analysis of hippocampal homogenate samples

Hippocampi and adjacent cortex were dissected and frozen until processing. Tissues were homogenized in radioimmunoprecipitation assay buffer, mechanically triturated, and shaken for 2 hours at 4°C. Lysates were centrifuged for 20 min at 4°C, and the supernatant was used for ELISAs (BMS6002 and BMS603–2, Invitrogen Life Technologies) and protein quantification by BCA assay. Cytokine concentrations were normalized to total protein. All assays were performed according to the manufacturer’s protocols.

Mouse behavioral analysis

Orai1^fl/fl and Orai1^{fl/fl Cx3CR1-Cre/ERT2} mice (10 to 12 weeks old) were group housed and randomly assigned to receive intraperitoneal injections of LPS (1 mg/kg) or saline. Mice were assessed for sickness behavior using a previously described rubric (86) at 6 and 24 hours after LPS administration. Locomotor activity was first evaluated with the OFT at 6 hours postinjection to quantify LPS-induced sickness behavior. Behavioral testing beginning at 24 hours postinjection was performed in the after order for all cohorts: OFT, elevated zero maze, TST, FST, and SPT. Data from mice were excluded if the tracking system failed, if animals fell from the zero maze, or if they climbed their tails during the TST.

For the OFT, mice were placed in an 80-cm square chamber and allowed to freely explore for 6 min while being video recorded. LimeLight software (Actimetrics) tracked total distance traveled and time spent in the center zone. MSSs were assessed using a previously established rubric (86) for changes in coat appearance, activity level, respiration rate, respiration quality, eye condition, responsiveness, and consciousness. For the elevated zero maze, mice were placed in a closed arm and allowed to explore for 6 min. Movements were recorded and analyzed with LimeLight software to measure the amount of time spent in open versus closed arms. In the TST, mice were suspended by the tail with laboratory tape, with a 3.5-cm clear plastic tube placed over the proximal tail to prevent climbing back up. Sessions lasted 6 min and were video recorded. Immobility time during the first 6 min was scored by a blinded experimenter. For the FST, mice were placed in a cylindrical glass vessel [8-inch (20.32-cm) diameter, 8-inch (20.32-cm) water depth, 25°C]. Each session lasted 6 min; immobility was scored during the final 4 min by a blinded experimenter. Mice were dried and returned to their home cage immediately after testing. For the SPT, mice were singly housed and given two bottles, one with 1% sucrose solution and the other with tap water, placed randomly in the cage. After 16 hours, both bottles were weighed, and sucrose preference was calculated as the percentage of sucrose solution consumed relative to total fluid intake.

Statistical analysis

The statistical tests performed for data analysis are indicated in the corresponding figure legends. All data are expressed as means ± SEM. For all data presented, n is the number of mice or cells used. Statistical analysis was done with Origin2024b with a confidence interval of 95%, and results with P < 0.05 were considered statistically significant. For datasets with two groups, statistical analysis was performed with a two-tailed unpaired t test. For datasets with greater than two groups, a two-way analysis of variance (ANOVA) followed by a Tukey post hoc test was performed to compare groups and genotypes (specified in the figure legends). The data analysis for RNA-seq and metabolomics analysis is described in relevant sections.

Supplementary Material

MDAR Reproducibility Checklist

NIHMS2190650-supplement-MDAR_Reproducibility_Checklist.pdf^{(454KB, pdf)}

NIHMS2190650-supplement-2.pdf^{(22.3MB, pdf)}

The PDF file includes:

Figs. S1 to S13

Tables S1 to S4

Other Supplementary Material for this manuscript includes the following:

MDAR Reproducibility checklist

Acknowledgments:

We are grateful to M. Novakovic for technical and experimental advice, H. Hale for help with animal care and microglial cell count analysis, and members of the Prakriya laboratory for helpful discussions during this work. Metabolomics services were performed by the Metabolomics Core Facility at Robert H. Lurie Comprehensive Cancer Center (supported by NCI CCSG P30 CA060553) of Northwestern University. We thank P. Gao, X. G. Pérez-Leonor, D. Phan, and G. Rerko for assistance with processing of samples for RNA-seq and metabolomics analysis.

Funding:

This work was supported by NIH grant R35NS132349 to M.P. K.E.D. was supported, in part, by a Julius Kahn predoctoral fellowship. R.A.G. was supported by the Kimberly Querrey Fellowship in Data Science and the Schmidt Science Fellows program. M.W.S. was supported by Canadian Institutes of Health Research (CIHR), FDN-154336 and PJT-191976.

Footnotes

Competing interests: The authors declare that they have no competing interests.

Data, code, and materials availability:

The RNA-seq and metabolomics data and original code have been deposited into databases at Zenodo and GitHub and are publicly available at https://zenodo.org/records/18495873. Microscopy, qPCR, and behavioral data will be shared by the lead contact upon request. Custom scripts for differential gene expression analysis can be found at https://github.com/NUPulmonary/utils. The RNA-seq data are also available on the Gene Expression Omnibus (GEO) under the accession number GSE310141. Source data (provided as Excel files) for the figures are available in Zenodo. All other data needed to evaluate the conclusions in the paper are present in the paper or the Supplementary Materials. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. All materials and reagents are commercially available or will be supplied upon reasonable request.

REFERENCES AND NOTES

1.Salter MW, Stevens B, Microglia emerge as central players in brain disease. Nat. Med. 23, 1018–1027 (2017). [DOI] [PubMed] [Google Scholar]
2.Li Q, Barres BA, Microglia and macrophages in brain homeostasis and disease. Nat. Rev. Immunol. 18, 225–242 (2018). [DOI] [PubMed] [Google Scholar]
3.Streit WJ, Walter SA, Pennell NA, Reactive microgliosis. Prog. Neurobiol. 57, 563–581 (1999). [DOI] [PubMed] [Google Scholar]
4.Beurel E, Toups M, Nemeroff CB, The bidirectional relationship of depression and inflammation: Double trouble. Neuron 107, 234–256 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Li B, Yang W, Ge T, Wang Y, Cui R, Stress induced microglial activation contributes to depression. Pharmacol. Res. 179, 106145 (2022). [DOI] [PubMed] [Google Scholar]
6.Chen G, Zhang YQ, Qadri YJ, Serhan CN, Ji RR, Microglia in pain: Detrimental and protective roles in pathogenesis and resolution of pain. Neuron 100, 1292–1311 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Butovsky O, Weiner HL, Microglial signatures and their role in health and disease. Nat. Rev. Neurosci. 19, 622–635 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Stebbing MJ, Cottee JM, Rana I, The role of ion channels in microglial activation and proliferation – A complex interplay between ligand-gated ion channels, K⁺ channels, and intracellular Ca²⁺. Front. Immunol. 6, 497 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Umpierre AD, Bystrom LL, Ying Y, Liu YU, Worrell G, Wu LJ, Microglial calcium signaling is attuned to neuronal activity in awake mice. eLife 9, e56502 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Färber K, Kettenmann H, Functional role of calcium signals for microglial function. Glia 54, 656–665 (2006). [DOI] [PubMed] [Google Scholar]
11.Möller T, Calcium signaling in microglial cells. Glia 40, 184–194 (2002). [DOI] [PubMed] [Google Scholar]
12.Umpierre AD, Li B, Ayasoufi K, Simon WL, Zhao S, Xie M, Thyen G, Hur B, Zheng J, Liang Y, Bosco DB, Maynes MA, Wu Z, Yu X, Sung J, Johnson AJ, Li Y, Wu LJ, Microglial P2Y6 calcium signaling promotes phagocytosis and shapes neuroimmune responses in epileptogenesis. Neuron 112, 1959–1977.e10 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Korshunov KS, Prakriya M, Store-operated calcium channels in the nervous system. Annu. Rev. Physiol. 87, 173–199 (2025). [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Feske S, CRAC channels and disease – From human CRAC channelopathies and animal models to novel drugs. Cell Calcium 80, 112–116 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Prakriya M, Lewis RS, Store-operated calcium channels. Physiol. Rev. 95, 1383–1436 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Emrich SM, Yoast RE, Trebak M, Physiological functions of CRAC channels. Annu. Rev. Physiol. 84, 355–379 (2022). [DOI] [PubMed] [Google Scholar]
17.Vaeth M, Kahlfuss S, Feske S, CRAC channels and calcium signaling in T cell-mediated immunity. Trends Immunol. 41, 878–901 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Novakovic MM, Korshunov KS, Grant RA, Martin ME, Valencia HA, Budinger GRS, Radulovic J, Prakriya M, Astrocyte reactivity and inflammation-induced depression-like behaviors are regulated by Orai1 calcium channels. Nat. Commun. 14, 5500 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Courjaret R, Prakriya M, Machaca K, SOCE as a regulator of neuronal activity. J. Physiol. 602, 1449–1462 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Maneshi MM, Toth AB, Ishii T, Hori K, Tsujikawa S, Shum AK, Shrestha N, Yamashita M, Miller RJ, Radulovic J, Swanson GT, Prakriya M, Orai1 channels are essential for amplification of glutamate-evoked Ca²⁺ signals in dendritic spines to regulate working and associative memory. Cell Rep. 33, 108464 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Heo DK, Lim HM, Nam JH, Lee MG, Kim JY, Regulation of phagocytosis and cytokine secretion by store-operated calcium entry in primary isolated murine microglia. Cell. Signal. 27, 177–186 (2015). [DOI] [PubMed] [Google Scholar]
22.Tsujikawa S, DeMeulenaere KE, Centeno MV, Ghazisaeidi S, Martin ME, Tapies MR, Maneshi MM, Yamashita M, Stauderman KA, Apkarian AV, Salter MW, Prakriya M, Regulation of neuropathic pain by microglial Orai1 channels. Sci. Adv. 9, eade7002 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Toth AB, Hori K, Novakovic MM, Bernstein NG, Lambot L, Prakriya M, CRAC channels regulate astrocyte Ca²⁺ signaling and gliotransmitter release to modulate hippocampal GABAergic transmission. Sci. Signal. 12, eaaw5450 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Novakovic MM, Prakriya M, Calcium signaling at the interface between astrocytes and brain inflammation. Curr. Opin. Neurobiol. 90, 102940 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Zhao XF, Alam MM, Liao Y, Huang T, Mathur R, Zhu X, Huang Y, Targeting microglia using Cx3cr1-Cre lines: Revisiting the specificity. eNeuro 6, ENEURO.0114–19.2019 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Riazi K, Galic MA, Kentner AC, Reid AY, Sharkey KA, Pittman QJ, Microglia-dependent alteration of glutamatergic synaptic transmission and plasticity in the hippocampus during peripheral inflammation. J. Neurosci. 35, 4942–4952 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Basilico B, Ferrucci L, Ratano P, Golia MT, Grimaldi A, Rosito M, Ferretti V, Reverte I, Sanchini C, Marrone MC, Giubettini M, De Turris V, Salerno D, Garofalo S, St-Pierre M-K, Carrier M, Renzi M, Pagani F, Modi B, Raspa M, Scavizzi F, Gross CT, Marinelli S, Tremblay M-È, Caprioli D, Maggi L, Limatola C, Di Angelantonio S, Ragozzino D, Microglia control glutamatergic synapses in the adult mouse hippocampus. Glia 70, 173–195 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Kettenmann H, Hanisch UK, Noda M, Verkhratsky A, Physiology of microglia. Physiol. Rev. 91, 461–553 (2011). [DOI] [PubMed] [Google Scholar]
29.Tan YL, Yuan Y, Tian L, Microglial regional heterogeneity and its role in the brain. Mol. Psychiatry 25, 351–367 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Parkhurst CN, Yang G, Ninan I, Savas JN, Yates III JR, Lafaille JJ, Hempstead BL, Littman DR, Gan W-B, Microglia promote learning-dependent synapse formation through brain-derived neurotrophic factor. Cell 155, 1596–1609 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Reu P, Khosravi A, Bernard S, Mold JE, Salehpour M, Alkass K, Perl S, Tisdale J, Possnert G, Druid H, Frisen J, The lifespan and turnover of microglia in the human brain. Cell Rep. 20, 779–784 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Yoast RE, Emrich SM, Zhang X, Xin P, Johnson MT, Fike AJ, Walter V, Hempel N, Yule DI, Sneyd J, Gill DL, Trebak M, The native ORAI channel trio underlies the diversity of Ca²⁺ signaling events. Nat. Commun. 11, 2444 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Wang L, Noyer L, Wang YH, Tao AY, Li W, Zhu J, Saavedra P, Hoda ST, Yang J, Feske S, ORAI3 is dispensable for store-operated Ca²⁺ entry and immune responses by lymphocytes and macrophages. J. Gen. Physiol. 154, e202213104 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Trang T, Beggs S, Salter MW, ATP receptors gate microglia signaling in neuropathic pain. Exp. Neurol. 234, 354–361 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Bernardo A, Minghetti L, PPAR-γ agonists as regulators of microglial activation and brain inflammation. Curr. Pharm. Des. 12, 93–109 (2006). [DOI] [PubMed] [Google Scholar]
36.Yang Z, Ming X-F, Functions of arginase isoforms in macrophage inflammatory responses: Impact on cardiovascular diseases and metabolic disorders. Front. Immunol. 5, 533 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Chang LC, Chiang SK, Chen SE, Hung MC, Targeting 2-oxoglutarate dehydrogenase for cancer treatment. Am. J. Cancer Res. 12, 1436–1455 (2022). [PMC free article] [PubMed] [Google Scholar]
38.Huo J, Molkentin JD, MCU genetically altered mice suggest how mitochondrial Ca²⁺ regulates metabolism. Trends Endocrinol. Metab. 35, 918–928 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Sun L, Yang X, Yuan Z, Wang H, Metabolic reprogramming in immune response and tissue inflammation. Arterioscler. Thromb. Vasc. Biol. 40, 1990–2001 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Sabogal-Guáqueta AM, Marmolejo-Garza A, Trombetta-Lima M, Oun A, Hunneman J, Chen T, Koistinaho J, Lehtonen S, Kortholt A, Wolters JC, Bakker BM, Eggen BJL, Boddeke E, Dolga A, Species-specific metabolic reprogramming in human and mouse microglia during inflammatory pathway induction. Nat. Commun. 14, 6454 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Chandel NS, Signaling and metabolism. Cold Spring Harb. Perspect. Biol. 13, a040600 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Mehta MM, Weinberg SE, Chandel NS, Mitochondrial control of immunity: Beyond ATP. Nat. Rev. Immunol. 17, 608–620 (2017). [DOI] [PubMed] [Google Scholar]
43.Jouaville LS, Pinton P, Bastianutto C, Rutter GA, Rizzuto R, Regulation of mitochondrial ATP synthesis by calcium: Evidence for a long-term metabolic priming. Proc. Natl. Acad. Sci. U.S.A. 96, 13807–13812 (1999). [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Horvat A, Muhič M, Smolič T, Begić E, Zorec R, Kreft M, Vardjan N, Ca²⁺ as the prime trigger of aerobic glycolysis in astrocytes. Cell Calcium 95, 102368 (2021). [DOI] [PubMed] [Google Scholar]
45.Wang Y, Tao A, Vaeth M, Feske S, Calcium regulation of T cell metabolism. Curr. Opin. Physio. 17, 207–223 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Zhu X, Guo Y, Liu Z, Yang J, Tang H, Wang Y, Itaconic acid exerts anti-inflammatory and antibacterial effects via promoting pentose phosphate pathway to produce ROS. Sci. Rep. 11, 18173 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Mills EL, Ryan DG, Prag HA, Dikovskaya D, Menon D, Zaslona Z, Jedrychowski MP, Costa ASH, Higgins M, Hams E, Szpyt J, Runtsch MC, King MS, McGouran JF, Fischer R, Kessler BM, McGettrick AF, Hughes MM, Carroll RG, Booty LM, Knatko EV, Meakin PJ, Ashford MLJ, Modis LK, Brunori G, Sévin DC, Fallon PG, Caldwell ST, Kunji ERS, Chouchani ET, Frezza C, Dinkova-Kostova AT, Hartley RC, Murphy MP, O’Neill LA, Itaconate is an anti-inflammatory metabolite that activates Nrf2 via alkylation of KEAP1. Nature 556, 113–117 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Shi X, Zhou H, Wei J, Mo W, Li Q, Lv X, The signaling pathways and therapeutic potential of itaconate to alleviate inflammation and oxidative stress in inflammatory diseases. Redox Biol. 58, 102553 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Steinberg GR, Carling D, AMP-activated protein kinase: The current landscape for drug development. Nat. Rev. Drug Discov. 18, 527–551 (2019). [DOI] [PubMed] [Google Scholar]
50.Zhao Y, Zhang J, Zheng Y, Zhang Y, Zhang XJ, Wang H, Du Y, Guan J, Wang X, Fu J, NAD+ improves cognitive function and reduces neuroinflammation by ameliorating mitochondrial damage and decreasing ROS production in chronic cerebral hypoperfusion models through Sirt1/PGC-1α pathway. J. Neuroinflammation 18, 207 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Velagapudi R, El-Bakoush A, Lepiarz I, Ogunrinade F, Olajide OA, AMPK and SIRT1 activation contribute to inhibition of neuroinflammation by thymoquinone in BV2 microglia. Mol. Cell. Biochem. 435, 149–162 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Batista CRA, Gomes GF, Candelario-Jalil E, Fiebich BL, de Oliveira ACP, Lipopolysaccharide-induced neuroinflammation as a bridge to understand neurodegeneration. Int. J. Mol. Sci. 20, 2293 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Lu YC, Yeh WC, Ohashi PS, LPS/TLR4 signal transduction pathway. Cytokine 42, 145–151 (2008). [DOI] [PubMed] [Google Scholar]
54.Zhang J, Rong P, Zhang L, He H, Zhou T, Fan Y, Mo L, Zhao Q, Han Y, Li S, Wang Y, Yan W, Chen H, You Z, IL4-driven microglia modulate stress resilience through BDNF-dependent neurogenesis. Sci. Adv. 7, eabb9888 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Elmore MR, Najafi AR, Koike MA, Dagher NN, Spangenberg EE, Rice RA, Kitazawa M, Matusow B, Nguyen H, West BL, Green KN, Colony-stimulating factor 1 receptor signaling is necessary for microglia viability, unmasking a microglia progenitor cell in the adult brain. Neuron 82, 380–397 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Lin LF, Doherty DH, Lile JD, Bektesh S, Collins F, GDNF: A glial cell line-derived neurotrophic factor for midbrain dopaminergic neurons. Science 260, 1130–1132 (1993). [DOI] [PubMed] [Google Scholar]
57.Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, Cheng JX, Murre C, Singh H, Glass CK, Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Berry CT, May MJ, Freedman BD, STIM- and Orai-mediated calcium entry controls NF-κB activity and function in lymphocytes. Cell Calcium 74, 131–143 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Shiratori-Hayashi M, Yamaguchi C, Eguchi K, Shiraishi Y, Kohno K, Mikoshiba K, Inoue K, Nishida M, Tsuda M, Astrocytic STAT3 activation and chronic itch require IP3R1/TRPC-dependent Ca²⁺ signals in mice. J. Allergy Clin. Immunol. 147, 1341–1353 (2021). [DOI] [PubMed] [Google Scholar]
60.Hillmer EJ, Zhang H, Li HS, Watowich SS, STAT3 signaling in immunity. Cytokine Growth Factor Rev. 31, 1–15 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Zhang Q, Lenardo MJ, Baltimore D, 30 years of NF-κB: A blossoming of relevance to human pathobiology. Cell 168, 37–57 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
62.da Silva AAF, Fiadeiro MB, Bernardino LI, Fonseca CSP, Baltazar GMF, Cristóvão ACB, Lipopolysaccharide-induced animal models for neuroinflammation - An overview. J. Neuroimmunol. 387, 578273 (2024). [DOI] [PubMed] [Google Scholar]
63.Yirmiya R, Endotoxin produces a depressive-like episode in rats. Brain Res. 711, 163–174 (1996). [DOI] [PubMed] [Google Scholar]
64.Krishnan V, Nestler EJ, Animal models of depression: Molecular perspectives. Curr. Top. Behav. Neurosci. 7, 121–147 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
65.DellaGioia N, Hannestad J, A critical review of human endotoxin administration as an experimental paradigm of depression. Neurosci. Biobehav. Rev. 34, 130–143 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
66.Banks WA, Robinson SM, Minimal penetration of lipopolysaccharide across the murine blood-brain barrier. Brain Behav. Immun. 24, 102–109 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
67.Geloso MC, Zupo L, Corvino V, Crosstalk between peripheral inflammation and brain: Focus on the responses of microglia and astrocytes to peripheral challenge. Neurochem. Int. 180, 105872 (2024). [DOI] [PubMed] [Google Scholar]
68.Sun Y, Koyama Y, Shimada S, Inflammation from peripheral organs to the brain: How does systemic inflammation cause neuroinflammation? Front. Aging Neurosci. 14, 903455 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
69.Somasundaram A, Shum AK, McBride HJ, Kessler JA, Feske S, Miller RJ, Prakriya M, Store-operated CRAC channels regulate gene expression and proliferation in neural progenitor cells. J. Neurosci. 34, 9107–9123 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
70.Takemura T, Makino S, Takao T, Asaba K, Suemaru S, Hashimoto K, Hypothalamic-pituitary-adrenocortical responses to single vs. repeated endotoxin lipopolysaccharide administration in the rat. Brain Res. 767, 181–191 (1997). [DOI] [PubMed] [Google Scholar]
71.Herman JP, McKlveen JM, Ghosal S, Kopp B, Wulsin A, Makinson R, Scheimann J, Myers B, Regulation of the hypothalamic-pituitary-adrenocortical stress response. Compr. Physiol. 6, 603–621 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
72.Chesnokova V, Pechnick RN, Wawrowsky K, Chronic peripheral inflammation, hippocampal neurogenesis, and behavior. Brain Behav. Immun. 58, 1–8 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
73.Jha MK, Jo M, Kim JH, Suk K, Microglia-astrocyte crosstalk: An intimate molecular conversation. Neuroscientist 25, 227–240 (2019). [DOI] [PubMed] [Google Scholar]
74.Linnerbauer M, Wheeler MA, Quintana FJ, Astrocyte crosstalk in CNS inflammation. Neuron 108, 608–622 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
75.Zipp F, Bittner S, Schafer DP, Cytokines as emerging regulators of central nervous system synapses. Immunity 56, 914–925 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
76.Belovicova K, Bogi E, Csatlosova K, Dubovicky M, Animal tests for anxiety-like and depression-like behavior in rats. Interdiscip. Toxicol. 10, 40–43 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
77.Yirmiya R, Pollak Y, Barak O, Avitsur R, Ovadia H, Bette M, Weihe E, Weidenfeld J, Effects of antidepressant drugs on the behavioral and physiological responses to lipopolysaccharide (LPS) in rodents. Neuropsychopharmacology 24, 531–544 (2001). [DOI] [PubMed] [Google Scholar]
78.Mello BSF, Chaves Filho AJM, Custódio CS, Cordeiro RC, Miyajima F, de Sousa FCF, Vasconcelos SMM, de Lucena DF, Macedo D, Sex influences in behavior and brain inflammatory and oxidative alterations in mice submitted to lipopolysaccharide-induced inflammatory model of depression. J. Neuroimmunol. 320, 133–142 (2018). [DOI] [PubMed] [Google Scholar]
79.Millett CE, Phillips BE, Saunders EFH, The sex-specific effects of LPS on depressive-like behavior and oxidative stress in the hippocampus of the mouse. Neuroscience 399, 77–88 (2019). [DOI] [PubMed] [Google Scholar]
80.Jiang X, Chen Z, Yu X, Chen J, Sun C, Jing C, Xu L, Liu F, Ni W, Chen L, Lipopolysaccharide-induced depression is associated with estrogen receptor-α/SIRT1/NF-κB signaling pathway in old female mice. Neurochem. Int. 148, 105097 (2021). [DOI] [PubMed] [Google Scholar]
81.Rossetti AC, Paladini MS, Trepci A, Mallien A, Riva MA, Gass P, Molteni R, Differential neuroinflammatory response in male and female mice: A role for BDNF. Front. Mol. Neurosci. 12, 166 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
82.Park H, Poo MM, Neurotrophin regulation of neural circuit development and function. Nat. Rev. Neurosci. 14, 7–23 (2013). [DOI] [PubMed] [Google Scholar]
83.Airaksinen MS, Saarma M, The GDNF family: Signalling, biological functions and therapeutic value. Nat. Rev. Neurosci. 3, 383–394 (2002). [DOI] [PubMed] [Google Scholar]
84.Sen T, Sen N, Noordhuis MG, Ravi R, Wu TC, Ha PK, Sidransky D, Hoque MO, OGDHL is a modifier of AKT-dependent signaling and NF-κB function. PLOS ONE 7, e48770 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
85.Bohlen CJ, Bennett FC, Tucker AF, Collins HY, Mulinyawe SB, Barres BA, Diverse requirements for microglial survival, specification, and function revealed by defined-medium cultures. Neuron 94, 759–773.e8 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
86.Shrum B, Anantha RV, Xu SX, Donnelly M, Haeryfar SM, McCormick JK, Mele T, A robust scoring system to evaluate sepsis severity in an animal model. BMC. Res. Notes 7, 233 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
87.Lively S, Schlichter LC, Microglia responses to pro-inflammatory stimuli (LPS, IFNγ+TNFα) and reprogramming by resolving cytokines (IL-4, IL-10). Front. Cell. Neurosci. 12, 215 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
88.Jurga AM, Paleczna M, Kuter KZ, Overview of general and discriminating markers of differential microglia phenotypes. Front. Cell. Neurosci. 14, 198 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

MDAR Reproducibility Checklist

NIHMS2190650-supplement-MDAR_Reproducibility_Checklist.pdf^{(454KB, pdf)}

NIHMS2190650-supplement-2.pdf^{(22.3MB, pdf)}

Data Availability Statement

[R1] 1.Salter MW, Stevens B, Microglia emerge as central players in brain disease. Nat. Med. 23, 1018–1027 (2017). [DOI] [PubMed] [Google Scholar]

[R2] 2.Li Q, Barres BA, Microglia and macrophages in brain homeostasis and disease. Nat. Rev. Immunol. 18, 225–242 (2018). [DOI] [PubMed] [Google Scholar]

[R3] 3.Streit WJ, Walter SA, Pennell NA, Reactive microgliosis. Prog. Neurobiol. 57, 563–581 (1999). [DOI] [PubMed] [Google Scholar]

[R4] 4.Beurel E, Toups M, Nemeroff CB, The bidirectional relationship of depression and inflammation: Double trouble. Neuron 107, 234–256 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R5] 5.Li B, Yang W, Ge T, Wang Y, Cui R, Stress induced microglial activation contributes to depression. Pharmacol. Res. 179, 106145 (2022). [DOI] [PubMed] [Google Scholar]

[R6] 6.Chen G, Zhang YQ, Qadri YJ, Serhan CN, Ji RR, Microglia in pain: Detrimental and protective roles in pathogenesis and resolution of pain. Neuron 100, 1292–1311 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R7] 7.Butovsky O, Weiner HL, Microglial signatures and their role in health and disease. Nat. Rev. Neurosci. 19, 622–635 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R8] 8.Stebbing MJ, Cottee JM, Rana I, The role of ion channels in microglial activation and proliferation – A complex interplay between ligand-gated ion channels, K⁺ channels, and intracellular Ca²⁺. Front. Immunol. 6, 497 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R9] 9.Umpierre AD, Bystrom LL, Ying Y, Liu YU, Worrell G, Wu LJ, Microglial calcium signaling is attuned to neuronal activity in awake mice. eLife 9, e56502 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R10] 10.Färber K, Kettenmann H, Functional role of calcium signals for microglial function. Glia 54, 656–665 (2006). [DOI] [PubMed] [Google Scholar]

[R11] 11.Möller T, Calcium signaling in microglial cells. Glia 40, 184–194 (2002). [DOI] [PubMed] [Google Scholar]

[R12] 12.Umpierre AD, Li B, Ayasoufi K, Simon WL, Zhao S, Xie M, Thyen G, Hur B, Zheng J, Liang Y, Bosco DB, Maynes MA, Wu Z, Yu X, Sung J, Johnson AJ, Li Y, Wu LJ, Microglial P2Y6 calcium signaling promotes phagocytosis and shapes neuroimmune responses in epileptogenesis. Neuron 112, 1959–1977.e10 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R13] 13.Korshunov KS, Prakriya M, Store-operated calcium channels in the nervous system. Annu. Rev. Physiol. 87, 173–199 (2025). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R14] 14.Feske S, CRAC channels and disease – From human CRAC channelopathies and animal models to novel drugs. Cell Calcium 80, 112–116 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R15] 15.Prakriya M, Lewis RS, Store-operated calcium channels. Physiol. Rev. 95, 1383–1436 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R16] 16.Emrich SM, Yoast RE, Trebak M, Physiological functions of CRAC channels. Annu. Rev. Physiol. 84, 355–379 (2022). [DOI] [PubMed] [Google Scholar]

[R17] 17.Vaeth M, Kahlfuss S, Feske S, CRAC channels and calcium signaling in T cell-mediated immunity. Trends Immunol. 41, 878–901 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R18] 18.Novakovic MM, Korshunov KS, Grant RA, Martin ME, Valencia HA, Budinger GRS, Radulovic J, Prakriya M, Astrocyte reactivity and inflammation-induced depression-like behaviors are regulated by Orai1 calcium channels. Nat. Commun. 14, 5500 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R19] 19.Courjaret R, Prakriya M, Machaca K, SOCE as a regulator of neuronal activity. J. Physiol. 602, 1449–1462 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R20] 20.Maneshi MM, Toth AB, Ishii T, Hori K, Tsujikawa S, Shum AK, Shrestha N, Yamashita M, Miller RJ, Radulovic J, Swanson GT, Prakriya M, Orai1 channels are essential for amplification of glutamate-evoked Ca²⁺ signals in dendritic spines to regulate working and associative memory. Cell Rep. 33, 108464 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R21] 21.Heo DK, Lim HM, Nam JH, Lee MG, Kim JY, Regulation of phagocytosis and cytokine secretion by store-operated calcium entry in primary isolated murine microglia. Cell. Signal. 27, 177–186 (2015). [DOI] [PubMed] [Google Scholar]

[R22] 22.Tsujikawa S, DeMeulenaere KE, Centeno MV, Ghazisaeidi S, Martin ME, Tapies MR, Maneshi MM, Yamashita M, Stauderman KA, Apkarian AV, Salter MW, Prakriya M, Regulation of neuropathic pain by microglial Orai1 channels. Sci. Adv. 9, eade7002 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R23] 23.Toth AB, Hori K, Novakovic MM, Bernstein NG, Lambot L, Prakriya M, CRAC channels regulate astrocyte Ca²⁺ signaling and gliotransmitter release to modulate hippocampal GABAergic transmission. Sci. Signal. 12, eaaw5450 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R24] 24.Novakovic MM, Prakriya M, Calcium signaling at the interface between astrocytes and brain inflammation. Curr. Opin. Neurobiol. 90, 102940 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R25] 25.Zhao XF, Alam MM, Liao Y, Huang T, Mathur R, Zhu X, Huang Y, Targeting microglia using Cx3cr1-Cre lines: Revisiting the specificity. eNeuro 6, ENEURO.0114–19.2019 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R26] 26.Riazi K, Galic MA, Kentner AC, Reid AY, Sharkey KA, Pittman QJ, Microglia-dependent alteration of glutamatergic synaptic transmission and plasticity in the hippocampus during peripheral inflammation. J. Neurosci. 35, 4942–4952 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R27] 27.Basilico B, Ferrucci L, Ratano P, Golia MT, Grimaldi A, Rosito M, Ferretti V, Reverte I, Sanchini C, Marrone MC, Giubettini M, De Turris V, Salerno D, Garofalo S, St-Pierre M-K, Carrier M, Renzi M, Pagani F, Modi B, Raspa M, Scavizzi F, Gross CT, Marinelli S, Tremblay M-È, Caprioli D, Maggi L, Limatola C, Di Angelantonio S, Ragozzino D, Microglia control glutamatergic synapses in the adult mouse hippocampus. Glia 70, 173–195 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R28] 28.Kettenmann H, Hanisch UK, Noda M, Verkhratsky A, Physiology of microglia. Physiol. Rev. 91, 461–553 (2011). [DOI] [PubMed] [Google Scholar]

[R29] 29.Tan YL, Yuan Y, Tian L, Microglial regional heterogeneity and its role in the brain. Mol. Psychiatry 25, 351–367 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R30] 30.Parkhurst CN, Yang G, Ninan I, Savas JN, Yates III JR, Lafaille JJ, Hempstead BL, Littman DR, Gan W-B, Microglia promote learning-dependent synapse formation through brain-derived neurotrophic factor. Cell 155, 1596–1609 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R31] 31.Reu P, Khosravi A, Bernard S, Mold JE, Salehpour M, Alkass K, Perl S, Tisdale J, Possnert G, Druid H, Frisen J, The lifespan and turnover of microglia in the human brain. Cell Rep. 20, 779–784 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R32] 32.Yoast RE, Emrich SM, Zhang X, Xin P, Johnson MT, Fike AJ, Walter V, Hempel N, Yule DI, Sneyd J, Gill DL, Trebak M, The native ORAI channel trio underlies the diversity of Ca²⁺ signaling events. Nat. Commun. 11, 2444 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R33] 33.Wang L, Noyer L, Wang YH, Tao AY, Li W, Zhu J, Saavedra P, Hoda ST, Yang J, Feske S, ORAI3 is dispensable for store-operated Ca²⁺ entry and immune responses by lymphocytes and macrophages. J. Gen. Physiol. 154, e202213104 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R34] 34.Trang T, Beggs S, Salter MW, ATP receptors gate microglia signaling in neuropathic pain. Exp. Neurol. 234, 354–361 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R35] 35.Bernardo A, Minghetti L, PPAR-γ agonists as regulators of microglial activation and brain inflammation. Curr. Pharm. Des. 12, 93–109 (2006). [DOI] [PubMed] [Google Scholar]

[R36] 36.Yang Z, Ming X-F, Functions of arginase isoforms in macrophage inflammatory responses: Impact on cardiovascular diseases and metabolic disorders. Front. Immunol. 5, 533 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R37] 37.Chang LC, Chiang SK, Chen SE, Hung MC, Targeting 2-oxoglutarate dehydrogenase for cancer treatment. Am. J. Cancer Res. 12, 1436–1455 (2022). [PMC free article] [PubMed] [Google Scholar]

[R38] 38.Huo J, Molkentin JD, MCU genetically altered mice suggest how mitochondrial Ca²⁺ regulates metabolism. Trends Endocrinol. Metab. 35, 918–928 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R39] 39.Sun L, Yang X, Yuan Z, Wang H, Metabolic reprogramming in immune response and tissue inflammation. Arterioscler. Thromb. Vasc. Biol. 40, 1990–2001 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R40] 40.Sabogal-Guáqueta AM, Marmolejo-Garza A, Trombetta-Lima M, Oun A, Hunneman J, Chen T, Koistinaho J, Lehtonen S, Kortholt A, Wolters JC, Bakker BM, Eggen BJL, Boddeke E, Dolga A, Species-specific metabolic reprogramming in human and mouse microglia during inflammatory pathway induction. Nat. Commun. 14, 6454 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R41] 41.Chandel NS, Signaling and metabolism. Cold Spring Harb. Perspect. Biol. 13, a040600 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R42] 42.Mehta MM, Weinberg SE, Chandel NS, Mitochondrial control of immunity: Beyond ATP. Nat. Rev. Immunol. 17, 608–620 (2017). [DOI] [PubMed] [Google Scholar]

[R43] 43.Jouaville LS, Pinton P, Bastianutto C, Rutter GA, Rizzuto R, Regulation of mitochondrial ATP synthesis by calcium: Evidence for a long-term metabolic priming. Proc. Natl. Acad. Sci. U.S.A. 96, 13807–13812 (1999). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R44] 44.Horvat A, Muhič M, Smolič T, Begić E, Zorec R, Kreft M, Vardjan N, Ca²⁺ as the prime trigger of aerobic glycolysis in astrocytes. Cell Calcium 95, 102368 (2021). [DOI] [PubMed] [Google Scholar]

[R45] 45.Wang Y, Tao A, Vaeth M, Feske S, Calcium regulation of T cell metabolism. Curr. Opin. Physio. 17, 207–223 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R46] 46.Zhu X, Guo Y, Liu Z, Yang J, Tang H, Wang Y, Itaconic acid exerts anti-inflammatory and antibacterial effects via promoting pentose phosphate pathway to produce ROS. Sci. Rep. 11, 18173 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R47] 47.Mills EL, Ryan DG, Prag HA, Dikovskaya D, Menon D, Zaslona Z, Jedrychowski MP, Costa ASH, Higgins M, Hams E, Szpyt J, Runtsch MC, King MS, McGouran JF, Fischer R, Kessler BM, McGettrick AF, Hughes MM, Carroll RG, Booty LM, Knatko EV, Meakin PJ, Ashford MLJ, Modis LK, Brunori G, Sévin DC, Fallon PG, Caldwell ST, Kunji ERS, Chouchani ET, Frezza C, Dinkova-Kostova AT, Hartley RC, Murphy MP, O’Neill LA, Itaconate is an anti-inflammatory metabolite that activates Nrf2 via alkylation of KEAP1. Nature 556, 113–117 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R48] 48.Shi X, Zhou H, Wei J, Mo W, Li Q, Lv X, The signaling pathways and therapeutic potential of itaconate to alleviate inflammation and oxidative stress in inflammatory diseases. Redox Biol. 58, 102553 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R49] 49.Steinberg GR, Carling D, AMP-activated protein kinase: The current landscape for drug development. Nat. Rev. Drug Discov. 18, 527–551 (2019). [DOI] [PubMed] [Google Scholar]

[R50] 50.Zhao Y, Zhang J, Zheng Y, Zhang Y, Zhang XJ, Wang H, Du Y, Guan J, Wang X, Fu J, NAD+ improves cognitive function and reduces neuroinflammation by ameliorating mitochondrial damage and decreasing ROS production in chronic cerebral hypoperfusion models through Sirt1/PGC-1α pathway. J. Neuroinflammation 18, 207 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R51] 51.Velagapudi R, El-Bakoush A, Lepiarz I, Ogunrinade F, Olajide OA, AMPK and SIRT1 activation contribute to inhibition of neuroinflammation by thymoquinone in BV2 microglia. Mol. Cell. Biochem. 435, 149–162 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R52] 52.Batista CRA, Gomes GF, Candelario-Jalil E, Fiebich BL, de Oliveira ACP, Lipopolysaccharide-induced neuroinflammation as a bridge to understand neurodegeneration. Int. J. Mol. Sci. 20, 2293 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R53] 53.Lu YC, Yeh WC, Ohashi PS, LPS/TLR4 signal transduction pathway. Cytokine 42, 145–151 (2008). [DOI] [PubMed] [Google Scholar]

[R54] 54.Zhang J, Rong P, Zhang L, He H, Zhou T, Fan Y, Mo L, Zhao Q, Han Y, Li S, Wang Y, Yan W, Chen H, You Z, IL4-driven microglia modulate stress resilience through BDNF-dependent neurogenesis. Sci. Adv. 7, eabb9888 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R55] 55.Elmore MR, Najafi AR, Koike MA, Dagher NN, Spangenberg EE, Rice RA, Kitazawa M, Matusow B, Nguyen H, West BL, Green KN, Colony-stimulating factor 1 receptor signaling is necessary for microglia viability, unmasking a microglia progenitor cell in the adult brain. Neuron 82, 380–397 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R56] 56.Lin LF, Doherty DH, Lile JD, Bektesh S, Collins F, GDNF: A glial cell line-derived neurotrophic factor for midbrain dopaminergic neurons. Science 260, 1130–1132 (1993). [DOI] [PubMed] [Google Scholar]

[R57] 57.Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, Cheng JX, Murre C, Singh H, Glass CK, Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R58] 58.Berry CT, May MJ, Freedman BD, STIM- and Orai-mediated calcium entry controls NF-κB activity and function in lymphocytes. Cell Calcium 74, 131–143 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R59] 59.Shiratori-Hayashi M, Yamaguchi C, Eguchi K, Shiraishi Y, Kohno K, Mikoshiba K, Inoue K, Nishida M, Tsuda M, Astrocytic STAT3 activation and chronic itch require IP3R1/TRPC-dependent Ca²⁺ signals in mice. J. Allergy Clin. Immunol. 147, 1341–1353 (2021). [DOI] [PubMed] [Google Scholar]

[R60] 60.Hillmer EJ, Zhang H, Li HS, Watowich SS, STAT3 signaling in immunity. Cytokine Growth Factor Rev. 31, 1–15 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R61] 61.Zhang Q, Lenardo MJ, Baltimore D, 30 years of NF-κB: A blossoming of relevance to human pathobiology. Cell 168, 37–57 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R62] 62.da Silva AAF, Fiadeiro MB, Bernardino LI, Fonseca CSP, Baltazar GMF, Cristóvão ACB, Lipopolysaccharide-induced animal models for neuroinflammation - An overview. J. Neuroimmunol. 387, 578273 (2024). [DOI] [PubMed] [Google Scholar]

[R63] 63.Yirmiya R, Endotoxin produces a depressive-like episode in rats. Brain Res. 711, 163–174 (1996). [DOI] [PubMed] [Google Scholar]

[R64] 64.Krishnan V, Nestler EJ, Animal models of depression: Molecular perspectives. Curr. Top. Behav. Neurosci. 7, 121–147 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R65] 65.DellaGioia N, Hannestad J, A critical review of human endotoxin administration as an experimental paradigm of depression. Neurosci. Biobehav. Rev. 34, 130–143 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R66] 66.Banks WA, Robinson SM, Minimal penetration of lipopolysaccharide across the murine blood-brain barrier. Brain Behav. Immun. 24, 102–109 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R67] 67.Geloso MC, Zupo L, Corvino V, Crosstalk between peripheral inflammation and brain: Focus on the responses of microglia and astrocytes to peripheral challenge. Neurochem. Int. 180, 105872 (2024). [DOI] [PubMed] [Google Scholar]

[R68] 68.Sun Y, Koyama Y, Shimada S, Inflammation from peripheral organs to the brain: How does systemic inflammation cause neuroinflammation? Front. Aging Neurosci. 14, 903455 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R69] 69.Somasundaram A, Shum AK, McBride HJ, Kessler JA, Feske S, Miller RJ, Prakriya M, Store-operated CRAC channels regulate gene expression and proliferation in neural progenitor cells. J. Neurosci. 34, 9107–9123 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R70] 70.Takemura T, Makino S, Takao T, Asaba K, Suemaru S, Hashimoto K, Hypothalamic-pituitary-adrenocortical responses to single vs. repeated endotoxin lipopolysaccharide administration in the rat. Brain Res. 767, 181–191 (1997). [DOI] [PubMed] [Google Scholar]

[R71] 71.Herman JP, McKlveen JM, Ghosal S, Kopp B, Wulsin A, Makinson R, Scheimann J, Myers B, Regulation of the hypothalamic-pituitary-adrenocortical stress response. Compr. Physiol. 6, 603–621 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R72] 72.Chesnokova V, Pechnick RN, Wawrowsky K, Chronic peripheral inflammation, hippocampal neurogenesis, and behavior. Brain Behav. Immun. 58, 1–8 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R73] 73.Jha MK, Jo M, Kim JH, Suk K, Microglia-astrocyte crosstalk: An intimate molecular conversation. Neuroscientist 25, 227–240 (2019). [DOI] [PubMed] [Google Scholar]

[R74] 74.Linnerbauer M, Wheeler MA, Quintana FJ, Astrocyte crosstalk in CNS inflammation. Neuron 108, 608–622 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R75] 75.Zipp F, Bittner S, Schafer DP, Cytokines as emerging regulators of central nervous system synapses. Immunity 56, 914–925 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R76] 76.Belovicova K, Bogi E, Csatlosova K, Dubovicky M, Animal tests for anxiety-like and depression-like behavior in rats. Interdiscip. Toxicol. 10, 40–43 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R77] 77.Yirmiya R, Pollak Y, Barak O, Avitsur R, Ovadia H, Bette M, Weihe E, Weidenfeld J, Effects of antidepressant drugs on the behavioral and physiological responses to lipopolysaccharide (LPS) in rodents. Neuropsychopharmacology 24, 531–544 (2001). [DOI] [PubMed] [Google Scholar]

[R78] 78.Mello BSF, Chaves Filho AJM, Custódio CS, Cordeiro RC, Miyajima F, de Sousa FCF, Vasconcelos SMM, de Lucena DF, Macedo D, Sex influences in behavior and brain inflammatory and oxidative alterations in mice submitted to lipopolysaccharide-induced inflammatory model of depression. J. Neuroimmunol. 320, 133–142 (2018). [DOI] [PubMed] [Google Scholar]

[R79] 79.Millett CE, Phillips BE, Saunders EFH, The sex-specific effects of LPS on depressive-like behavior and oxidative stress in the hippocampus of the mouse. Neuroscience 399, 77–88 (2019). [DOI] [PubMed] [Google Scholar]

[R80] 80.Jiang X, Chen Z, Yu X, Chen J, Sun C, Jing C, Xu L, Liu F, Ni W, Chen L, Lipopolysaccharide-induced depression is associated with estrogen receptor-α/SIRT1/NF-κB signaling pathway in old female mice. Neurochem. Int. 148, 105097 (2021). [DOI] [PubMed] [Google Scholar]

[R81] 81.Rossetti AC, Paladini MS, Trepci A, Mallien A, Riva MA, Gass P, Molteni R, Differential neuroinflammatory response in male and female mice: A role for BDNF. Front. Mol. Neurosci. 12, 166 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R82] 82.Park H, Poo MM, Neurotrophin regulation of neural circuit development and function. Nat. Rev. Neurosci. 14, 7–23 (2013). [DOI] [PubMed] [Google Scholar]

[R83] 83.Airaksinen MS, Saarma M, The GDNF family: Signalling, biological functions and therapeutic value. Nat. Rev. Neurosci. 3, 383–394 (2002). [DOI] [PubMed] [Google Scholar]

[R84] 84.Sen T, Sen N, Noordhuis MG, Ravi R, Wu TC, Ha PK, Sidransky D, Hoque MO, OGDHL is a modifier of AKT-dependent signaling and NF-κB function. PLOS ONE 7, e48770 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R85] 85.Bohlen CJ, Bennett FC, Tucker AF, Collins HY, Mulinyawe SB, Barres BA, Diverse requirements for microglial survival, specification, and function revealed by defined-medium cultures. Neuron 94, 759–773.e8 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R86] 86.Shrum B, Anantha RV, Xu SX, Donnelly M, Haeryfar SM, McCormick JK, Mele T, A robust scoring system to evaluate sepsis severity in an animal model. BMC. Res. Notes 7, 233 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R87] 87.Lively S, Schlichter LC, Microglia responses to pro-inflammatory stimuli (LPS, IFNγ+TNFα) and reprogramming by resolving cytokines (IL-4, IL-10). Front. Cell. Neurosci. 12, 215 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R88] 88.Jurga AM, Paleczna M, Kuter KZ, Overview of general and discriminating markers of differential microglia phenotypes. Front. Cell. Neurosci. 14, 198 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Microglial reactivity and neuroinflammation-driven changes in motivational behaviors are regulated by Orai1 calcium channels

Kaitlyn E DeMeulenaere

Rogan A Grant

Megan E Martin

Hiam A Valencia

Jelena Radulovic

Michael W Salter

Murali Prakriya

Abstract

Editor’s summary

INTRODUCTION

RESULTS

Orai1 is necessary for SOCE in hippocampal microglia

Fig. 1. Orai1 is essential for SOCE in murine hippocampal microglia.

Deletion of Orai1 suppresses microglial inflammatory pathways and up-regulates anti-inflammatory and proreparative markers

Fig. 2. RNA-seq analysis reveals inflammatory and metabolic pathways regulated by Orai1 in microglia.

Orai1 stimulates metabolic reprogramming associated with inflammatory responses

Fig. 3. Orai1 deletion alters metabolic pathways in microglia.

Orai1 promotes proinflammatory pathways activated by LPS signaling

Fig. 4. Deletion of Orai1 attenuates LPS-induced inflammatory gene expression in microglia.

Microglial Orai1 promotes proinflammatory and immune pathways after in vivo LPS challenge

Fig. 5. Microglial Orai1 regulates proinflammatory and immune pathways in response to peripheral LPS challenge.

Orai1 deletion lowers microglial and astrocyte reactivity and inflammatory cytokines in the brain after peripheral inflammation challenge

Fig. 6. Deletion of Orai1 attenuates LPS-induced glial reactivity and inflammatory cytokines in male mice.

Deletion of Orai1 in microglia mitigates inflammation-induced depression-like behaviors

Fig. 7. Microglial Orai1 cKO mice are protected against inflammation-induced motivational and affective behavioral changes.

DISCUSSION

Fig. 8. A schematic summarizing the role of Orai1-mediated Ca2+ signaling in microglial inflammatory responses and behavior.

MATERIALS AND METHODS

Mice

Primary neonate microglia cultures

Isolation of microglia from adult mice

FACS isolation of adult microglia for bulk RNA-seq

Wide-field Fura-2 Ca2+ imaging

Bulk RNA-seq analysis

Differential gene expression analysis

k-means clustering

GSEA and HOMER analysis

RNA-seq statistical analysis

Metabolomics

Two-step RT-qPCR analysis

Administration of LPS to adult mice

Immunohistochemistry

ELISA analysis of hippocampal homogenate samples

Mouse behavioral analysis

Statistical analysis

Supplementary Material

Acknowledgments:

Funding:

Footnotes

Data, code, and materials availability:

REFERENCES AND NOTES

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases

Fig. 8. A schematic summarizing the role of Orai1-mediated Ca²⁺ signaling in microglial inflammatory responses and behavior.

Wide-field Fura-2 Ca²⁺ imaging