Abstract

   Mitochondrial dysfunction is linked to age-associated inflammation or
   inflammaging, but underlying mechanisms are not understood. Analyses of
   700 human blood transcriptomes revealed clear signs of age-associated
   low-grade inflammation. Among changes in mitochondrial components, we
   found that the expression of mitochondrial calcium uniporter (MCU) and
   its regulatory subunit MICU1, genes central to mitochondrial Ca^2+
   (mCa^2+) signaling, correlated inversely with age. Indeed, mCa^2+
   uptake capacity of mouse macrophages decreased significantly with age.
   We show that in both human and mouse macrophages, reduced mCa^2+ uptake
   amplifies cytosolic Ca^2+ oscillations and potentiates downstream
   nuclear factor kappa B activation, which is central to inflammation.
   Our findings pinpoint the mitochondrial calcium uniporter complex as a
   keystone molecular apparatus that links age-related changes in
   mitochondrial physiology to systemic macrophage-mediated age-associated
   inflammation. The findings raise the exciting possibility that
   restoring mCa^2+ uptake capacity in tissue-resident macrophages may
   decrease inflammaging of specific organs and alleviate age-associated
   conditions such as neurodegenerative and cardiometabolic diseases.

   Subject terms: Inflammation, Mitochondria, Ageing, Ion channel
   signalling
     __________________________________________________________________

   Seegren et al. demonstrate that the mitochondrial calcium uptake
   complex is a key molecular apparatus that links age-related changes in
   mitochondrial physiology to systemic macrophage-mediated age-associated
   inflammation.

Main

   Inflammation is widely recognized as a key driver of aging^[44]1,[45]2.
   An age-associated low-grade, chronic inflammatory state promotes tissue
   damage and hence this process is referred to as inflammaging. The
   etiology of inflammaging is not understood but it is thought to involve
   an increase in the baseline inflammatory output by immune cells, as
   evident from higher cytokine levels and other inflammatory markers in
   the blood of aged humans^[46]3–[47]6. Inflammatory stimuli can
   originate from multiple sources: pathogens, resident microbiomes,
   tissue damage-associated inflammatory signals, and even spontaneous
   production of inflammatory molecules by senescent cells^[48]7–[49]9.
   Myeloid cells of the immune system, such as macrophages and
   neutrophils, are central players in inflammation and may contribute to
   inflammaging.

   Macrophages reside in every organ system and act as sentinel cells
   monitoring their environment for infection or injury^[50]10–[51]12. The
   inflammatory gene expression in macrophages is a highly regulated
   process with multiple checkpoints. The nuclear factor kappa B (NF-κB)
   family of dimeric transcription factors have an evolutionarily
   conserved and central role in inflammatory gene
   expression^[52]13,[53]14. Many studies have pointed to the salience of
   NF-κB to inflammaging^[54]15–[55]18. Analysis of age-related changes in
   gene expression in human and mouse tissues identified the NF-κB pathway
   as the most strongly associated transcriptional pathway to
   aging^[56]15. The secretion of high levels of pro-inflammatory
   cytokines in two different mouse models of accelerated aging was also
   found to be dependent on abnormal NF-κB activaton^[57]17. These studies
   suggest that a lowered threshold of NF-κB activation underlies
   inflammaging, but how this transpires is not understood. Many positive
   and negative signaling elements control the activation of NF-κB^[58]13.
   Among these regulatory checkpoints, the nuclear translocation and
   transcriptional activity of NF-κB is also controlled by cytosolic Ca^2+
   (cCa^2+) signaling^[59]19–[60]22.

   Ca^2+ is a ubiquitous and essential second messenger in cell
   biology^[61]23. Elevations in cCa^2+ trigger an influx of Ca^2+ into
   the mitochondrial matrix through the mitochondrial calcium uniporter
   (MCU), a Ca^2+-selective ion channel that resides in the mitochondrial
   inner membrane^[62]24–[63]30. The mitochondrial outer membrane is
   porous to ions, but the inner membrane has a resting membrane potential
   between −160 mV and −200 mV, relative to the cytosol^[64]24,[65]31.
   MICU1 (refs. ^[66]32,[67]33) and MICU2 (refs. ^[68]34), the EF-hand
   containing Ca^2+-sensitive regulatory subunits of MCU interact directly
   with MCU in the intermembrane space. Structural studies support the
   view that MCU–MICU1–MICU2 interactions are configured to have a
   switch-like sensitivity to [Ca^2+], enabling rapid mCa^2+ uptake when
   cytosolic [Ca^2+] increases beyond the resting range of
   ~10–100 nM^[69]35–[70]38. Because the mitochondrial matrix contains
   many metabolic enzymes that are regulated by Ca^2+, the mCa^2+
   signaling within the matrix has a profound effect on mitochondrial
   physiology and metabolism^[71]29,[72]39,[73]40. The cells of the
   vertebrate immune system use Ca^2+ signaling for an immediate-early
   response to antigenic and inflammatory stimuli—cCa^2+ elevations
   regulate the activation of both the innate and adaptive immune
   cells^[74]41. Recently, we revealed that mCa^2+ signaling functions as
   an electrometabolic switch to fuel macrophage-mediated phagosomal
   killing^[75]42. The process involves a fast two-step Ca^2+ relay to
   meet the bioenergetic demands of phagosomal killing. Additionally,
   recent reports have supported a role for the MCU and mCa^2+ in
   macrophage polarization^[76]43,[77]44, host defense^[78]42,[79]45 and
   tissue homeostasis^[80]46–[81]48. mCa^2+ is thus emerging as a central
   node for innate immunity and inflammatory responses. Here we report a
   surprising discovery that mCa^2+ uptake capacity of macrophages
   decreases progressively with age, and this is a major driver of
   inflammaging.

Results

Human blood transcriptomes reveal signs of age-associated low-grade
inflammation

   To gain insights into inflammaging, we mined the publicly available
   Genotype-Tissue Expression (GTEx) database
   ([82]https://gtexportal.org/)^[83]49 for tissue-specific gene
   expression across five different human age groups (Fig. [84]1a).
   Because mature red blood cells are anucleated and do not contain any
   appreciable amounts of mRNA, RNA sequencing (RNA-seq) of whole blood is
   a reasonable surrogate of combined gene expression in the white blood
   cells and platelets. Expression profile data were obtained for
   different tissues, binned into age groups, and then subjected to
   differential gene expression analysis using DESeq2 R package.
   Principle-component analysis (PCA) plots, from the five different age
   groups, revealed clear age-associated clustering (Fig. [85]1b). The
   variance in overall gene expression was greatest when we compared the
   youngest population (age 20–29 years) with the oldest population (age
   60–69 years; Fig. [86]1c), but the variance in overall gene expression
   was the least when we compared the second-oldest population (age 50–59
   years) to the oldest population (age 60–69 years; Extended Data Fig.
   [87]1a). These data substantiate the view that gene expression in the
   blood changes significantly with age. To derive further insights and to
   distill testable hypotheses for the etiology of inflammaging, we
   focused our analysis on differences between the youngest (age 20–29
   years) and oldest (age 60–69 years) samples. Gene-set enrichment
   analysis (GSEA) hallmark and GO pathway analyses of differentially
   expressed genes showed that the genes associated with inflammatory
   responses were upregulated in the older population (Fig. [88]1d and
   Extended Data Fig. [89]1b). Additionally, we observed a significant
   decrease in the gene expression related to oxidative phosphorylation, a
   mitochondrial process, in the older populations. Enrichment scores and
   gene ranks of inflammatory genes (Fig. [90]1e–g) and oxidative
   phosphorylation-associated genes (Fig. [91]1h) suggest a significant
   dysregulation in these pathways in the aged population. As shown in the
   heat map (Fig. [92]1i), genes associated with the NF-κB pathway were
   upregulated in the blood cells of older humans. Overall, these gene
   expression analyses show clear signs of age-associated chronic
   low-grade inflammation in the whole blood of human samples.

Fig. 1. Age-related changes in whole-blood gene expression are associated
with increased inflammatory gene transcription and decreased expression of
genes encoding mitochondrial Ca^2+ transport.

   [93]Fig. 1
   [94]Open in a new tab

   a, GTEx database mined for tissue-specific gene expression across five
   indicated age groups; note the color coding for the age groups. b,
   Left, PCA of whole-blood gene expression from every sample, color coded
   according to the age groups. Right, the same data were used and
   color-coded clusters from each age group were overlaid. Note the
   variance in gene expression from different age groups. c, PCA plots
   from b were used to show only samples from the youngest and oldest age
   groups analyzed. d, Hallmark GSEA based on differential gene expression
   between the oldest (60–69 years) and youngest (20–29 years) datasets.
   Pathways were ranked by P value and plotted on the x axis by the
   normalized enrichment scores. e, GSEA of GSEA hallmark pathway, TNF
   signaling via NF-κB, based on differential gene expression of oldest
   (60–69 years) versus youngest (20–29 years) GTEx samples. Enrichment
   scores are plotted on the y axis and genes ranked in the ordered
   dataset are plotted on the x axis. f, GSEA of the IL-2–STAT5 GSEA
   hallmark pathway. g, GSEA of inflammatory response GSEA hallmark
   pathway. h, GSEA of the oxidative phosphorylation GSEA hallmark
   pathway. i, Heat map of expression levels of genes associated with the
   TNF–NF-κB pathway. Expression values were calculated as a fold change
   from the 20–29-year age group. j, mitoXplorer Pathway analysis based on
   DSeq2 analysis of oldest (60–69 years) versus youngest (20–29 years)
   GTEx samples. k, Individual genes in the calcium signaling and
   transport pathway were identified from mitoXplorer pathway analysis
   based on DSeq2 analysis of oldest (60–69 years) versus youngest
   (20–29 years) GTEx samples. Fold change was determined as a relative
   change in 60–69-year compared to 20–29-year GTEX samples. l, MCU gene
   counts for each sample in the GTEx database sorted by age. Error bars
   reflect the s.e.m.; P values were calculated using ordinary one-way
   analysis of variance (ANOVA). m, MICU1 gene counts for each sample in
   GTEx database sorted by age. Error bars reflect the s.e.m.; P values
   were calculated using ordinary one-way ANOVA.

   [95]Source data

Extended Data Fig. 1. GTEx analysis of human whole blood transcriptomes.

   [96]Extended Data Fig. 1
   [97]Open in a new tab

   a. Variance in gene expression in whole blood of different age groups
   based on Principal Component analysis on GTEx samples. b. GO pathway
   enrichment analysis based on DSeq2 from GTEx samples. Differential gene
   expression is plotted for ages 60–69 vs 20–29. Significance determined
   by Dotplot function (clusterProfiler R package^[98]1). c. Pipeline used
   for the analysis of genes encoding for mitochondria-localized proteins
   (mito-genes). d. Principal component analysis on mito-genes from GTEx
   samples. Variance in gene expression is shown for individual age
   groups. e. Variance in gene expression shown for age 20–29 vs 60–69,
   30–39 vs 60–69, 40–49 vs 60–69, and 50–59 vs 60–69. f. Volcano plot of
   mito-genes expression levels in old (60–69y) vs young (20–29y) samples.
   Data normalization, dispersion estimates, and model fitting (negative
   binomial) were carried out with the DESeq function (DESeq2 R package).
   Wald statistics were used for the significance tests. g. MICU2, EMRE,
   and MCUB gene counts for each sample in GTEx database sorted by age.
   Error bars reflect SEM; p-values were calculated using one-way ANOVA.

   [99]Source data

Gene expression of mitochondrial Ca^2+ uptake machinery correlates inversely
with age

   Both inflammation and mitochondrial dysfunction are hallmarks of
   aging^[100]1, and we wondered if there was a relationship pertinent to
   inflammaging. For the analysis of age-related changes in mitochondrial
   function, we used mitoXplorer, an analysis and visualization tool
   specialized for genes associated with mitochondrial functions
   (mito-genes)^[101]50. In accordance with previous observations, we
   observed significant age-related changes in the expression of
   mito-genes (Extended Data Fig. [102]1c–f). We noted a decrease in the
   mito-genes associated with oxidative phosphorylation, calcium signaling
   and reactive oxygen species defense (Fig. [103]1j). The mito-genes
   associated with mitochondrial transcription, mitochondrial dynamics,
   pyruvate metabolism and amino acid metabolism were expressed at similar
   levels. The lipid metabolism, tricarboxylic acid (TCA) cycle and
   glycolysis genes were expressed at higher levels in the aged
   population. Because Ca^2+ signaling has a direct impact on inflammatory
   signaling in immune cells, we considered genes involved in mCa^2+
   signaling and found decreased expression of MCU, MICU1 and MICU2 (Fig.
   [104]1k). Moreover, the decrease in MCU and MICU1 expression was
   strongly associated with age, decreasing progressively as humans age
   (Fig. [105]1l,m). These observations suggested an age-associated
   dysregulation in mCa^2+ uptake in the blood-borne immune cells. This
   transcriptional dysregulation was observed for MCU, MICU1 and MICU2 but
   the gene expression of EMRE (SMDT1) and the dominant-negative regulator
   MCUB showed no significant change with age (Fig. [106]1l,m and Extended
   Data Fig. [107]1g). We wondered if such an age-related decrease in MCU
   is found in all human tissues. We checked different tissues in the
   age-stratified GTEx data we had mined and found that the age-associated
   decrease in MCU gene expression was only seen in a few tissues—heart,
   whole blood and cerebellum (Extended Data Fig. [108]2a). The vast
   majority of tissues did not show decreased MCU expression, and some
   tissues, skeletal muscle, adipose tissue and thyroid showed the
   opposite trend—MCU expression in these tissues increased with age. The
   participant death parameters are reported in the GTEx database on a
   four-point Hardy Scale (Extended Data Fig. [109]2b). We assessed MCU
   expression across the reported Hardy Scale and found that the MCU
   expression was higher in the most abundant category (death 0), compared
   to other death categories (Extended Data Fig. [110]2b). The
   participants in death category 0 were on a ventilator before their
   death. When we analyzed the whole-blood samples of participants binned
   in this category (death 0), we still observed an age-dependent decrease
   in MCU (Extended Data Fig. [111]2c). Together, these results suggest
   that mCa^2+ uptake capacity changes with age in some tissues, and
   likely contributes to age-related changes in the physiology of these
   tissues. From the standpoint of age-associated inflammation, the
   analyses put a spotlight on the key finding that in the blood,
   expression of MCU and MICU1 decrease progressively with age.

Extended Data Fig. 2. Age-associated changes in MCU expression in different
human tissues.

   [112]Extended Data Fig. 2
   [113]Open in a new tab

   a. Heat map for fold change gene reads of MCU across different tissues.
   b. MCU gene counts for each sample in GTEx database sorted by 4-point
   Hardy-Scale. Error bars reflect SEM; p-values were calculated using
   one-way ANOVA. c. MCU gene counts for each sample in GTEx database
   sorted by age for all death circumstance 0 on 4-point Hardy-Scale.
   Error bars reflect SEM; p-values were calculated using one-way ANOVA.

   [114]Source data

Reduced mitochondrial Ca^2+ uptake in macrophages derived from old mice

   The most abundant cell types in the human blood are myeloid cells,
   which are composed mainly of neutrophils and monocytes. Both of these
   cell types are short-lived in the blood but play a crucial role in
   inflammation. We reasoned that monocytes are more important for chronic
   low-grade inflammation because they can differentiate into macrophages
   and thereby sustain low-grade inflammation and inflammatory cascades
   over a relatively long period of time. Furthermore, all tissues and
   organs contain specialized resident macrophages, which are central to
   local inflammation and homeostasis. We also know that mCa^2+ uptake
   plays an important role in macrophage-mediated fungal killing^[115]42.
   Considering these aspects, we focused our investigation on how mCa^2+
   signaling might affect macrophage-mediated inflammation. First, we
   confirmed that the age-associated decrease in Mcu expression was
   recapitulated in mouse bone marrow-derived macrophages from older mice
   (BMDMs-O) when compared to those from the young mice (BMDMs-Y; Fig.
   [116]2a). Importantly, the reduced gene expression resulted in
   decreased MCU protein levels (Extended Data Fig. [117]3a). MICU1
   protein levels were unchanged (Extended Data Fig. [118]3a). We wondered
   if this transcriptional defect was a result of macrophage
   differentiation ex vivo or intrinsic to bone marrow progenitors. We
   measured the gene expression of Mcu and its regulatory subunits in
   undifferentiated bone marrow cells (BMCs) and bone marrow-derived
   macrophages (BMDMs) isolated from young (15–25 weeks) and old (80–90
   weeks) mice (Extended Data Fig. [119]3b). In the old BMCs, we found a
   significant decrease in the expression of Mcu, Micu2 and Emre. In the
   BMDMs derived from old BMCs, we found a significant decrease in the
   gene expression of Mcu, Emre and Mcub. These results indicate that the
   bone marrow progenitors undergo substantial changes in the expression
   of MCU complex components, and especially in the expression of Mcu.
   Changes in regulatory subunit composition and expression can affect
   mCa^2+ uptake capacity^[120]51. We reasoned that gross changes in the
   stoichiometry of the MCU complex would affect its protein mobility when
   resolved on a non-reducing gel. However, the mobility was identical in
   BMDMs-O and BMDMs-Y (Extended Data Fig. [121]3c). Stripping the
   membrane and immunoblotting for MICU1 showed comparable levels of MICU1
   at the same mobility at MCU, although we found MICU1 in other complexes
   as well (Extended Data Fig. [122]3c). Next, we tested the most obvious
   hypothesis that macrophages exhibit an age-dependent decrease in their
   mCa^2+ uptake. The basic technical design of this assay is to add Ca^2+
   to macrophages permeabilized with digitonin, and as the mitochondria
   take up the added Ca^2+, its loss from the bath solution is reported by
   the reduction in the fluorescence of calcium green-5N, a small-molecule
   Ca^2+ indicator in the bath solution. We show that BMDMs derived from
   the young mice exhibited robust mCa^2+ uptake, but this process was
   significantly impaired in the BMDMs-O. The representative traces are
   shown in Fig. [123]2b and a quantification of the percentage of the
   added Ca^2+ taken up by the mitochondria is shown in Fig. [124]2c. The
   addition of the mitochondrial uncoupler FCCP stops the Ca^2+ uptake and
   even reverses it (Fig. [125]2b), indicating that the mCa^2+ uptake is
   driven by the membrane potential of the mitochondrial inner membrane.
   Similarly, Ruthenium red (10 μM), a known blocker of
   MCU^[126]24,[127]31, abrogates mCa^2+ uptake, showing that the process
   is largely dependent on MCU. This age-associated reduction in mCa^2+
   uptake was found in both females and males (Fig. [128]2d). When we
   pulsed a much lower dose of Ca^2+ (1 µM), the mCa^2+ uptake in BMDMs-O
   was comparable for the first two pulses but started to lag behind
   BMDMs-Y after that (Extended Data Fig. [129]3d), consistent with
   impaired mCa^2+ uptake. To determine if defects in mCa^2+ uptake were a
   result of decreased mitochondrial membrane potential, we measured
   mitochondrial membrane potential in BMDMs-O and BMDMs-Y with TMRM, at
   baseline and after zymosan stimulation (Extended Data Fig. [130]4a).
   Surprisingly, BMDMs-O showed a modest hyperpolarization compared to
   BMDMs-Y suggesting the defect in mCa^2+ uptake is independent of
   resting membrane potential (Extended Data Fig. [131]4a). We also
   measured ATP levels in BMDMs-Y and BMDMs-O but found no significant
   differences in ATP levels (Extended Data Fig. [132]4b). Besides an
   evaluation of mCa^2+ uptake, we also quantified mitochondrial numbers
   and morphology. We immunostained for TOM20 and then applied an
   automated image processing software to quantify mitochondrial numbers
   and morphology of confocal images^[133]52. Comparing BMDMs-Y and
   BMDMs-O in this manner, we found a modest reduction in mitochondrial
   numbers but no significant differences in mitochondrial area, roundness
   and branches (Extended Data Fig. [134]4c–g). Overall, the results show
   conclusively that the macrophages in old mice have a significant defect
   in mCa^2+ uptake, and this is attributable, at least in part, to a
   substantial decrease in MCU protein levels and to modest changes in
   mitochondrial numbers. Next, we focused on understanding the functional
   implications of this age-associated defect in mCa^2+ uptake in
   macrophages.

Fig. 2. Macrophages generated from aged mice display decreased mitochondrial
Ca^2+ uptake.

   [135]Fig. 2
   [136]Open in a new tab

   a, Mcu expression in BMDMs. N = 12 biological replicates, from four
   mice. Error bars reflect the s.e.m.; P = 0.0016 according to Welch’s
   t-test, two-tailed. b, Representative traces of mCa^2+ uptake. c,
   Quantification of b. N = 28 biological replicates. Error bars reflect
   the s.e.m.; P values were calculated using ordinary one-way ANOVA. RuR,
   Ruthenium red. d, mCa^2+ uptake data shown in c—segregated by sex.
   Error bars reflect the s.e.m.; P values were calculated using Welch’s
   t-test, two-tailed. e, Representative cCa^2+ oscillations. f, Maximum
   cCa^2+. N = 88 cells, three independent experiments. Whiskers represent
   the minimum to maximum values for each dataset. The box represents the
   75th and 25th percentiles. The line is the median; P < 0.0001 according
   to one-way ANOVA. g, CALIMA spatiotemporal Ca^2+ dynamics. h, Number of
   oscillations in individual cells. N = 88 cells, three independent
   experiments. Whiskers represent the minimum to maximum values for each
   dataset. The box represents the 75th and 25th percentiles. Line is at
   the median; P < 0.0001 according to Welch’s t-test, two-tailed. i,
   Oscillation length in individual cells. N = 88 cells, three independent
   experiments. Whiskers represent the minimum to maximum values for each
   dataset. The box represents the 75th and 25th percentiles. Line is at
   the median; P < 0.0001 according to Welch’s t-test, two-tailed. NS, not
   significant.

   [137]Source data

Extended Data Fig. 3. Expression of MCU components in BMDMs.

   [138]Extended Data Fig. 3
   [139]Open in a new tab

   a. Western blot analysis of MCU, TOM20, and MICU1 protein. N = 5–15
   mice, error bars reflect SEM; p-value determined by Welch’s t-test,
   two-tailed. b. Quantitative-PCR of Mcu and its regulatory subunits. N =
   3 mice. Error bars reflect SEM; p-value determined by Welch’s t-test,
   two-tailed. c. Resolution of MCU complex in non-reducing conditions and
   immunoblotting for MCU and MICU1, N = 1 mouse. d. Pulsed mitochondrial
   Ca^2+ uptake. N = 15 biological replicates.

   [140]Source data

Extended Data Fig. 4. Analysis of mitochondria and Ca^2+ responses in BMDMs.

   [141]Extended Data Fig. 4
   [142]Open in a new tab

   a. Mitochondrial membrane potential. N = 90 cells, 3 independent
   experiments. Error bars reflect SEM; p-value determined by one-way
   ANOVA. b. Normalized luminescent measurements of ATP. N = 6 biological
   repeats. Error bars reflect SEM; p-value determined by one-way ANOVA.
   c. Mitochondrial counts. N = 30–45 cells, 3 independent experiments.
   Error bars reflect SEM; p-value significance according to Welch’s
   t-test, two-tailed. d. Mitochondrial area. N = 30–45 cells, 3
   independent experiments. Error bars reflect SEM; not statistically
   significant according to Welch’s t-test, two-tailed. e. Mitochondrial
   roundness. N = 30–45 cells, 3 independent experiments. Error bars
   reflect SEM; not statistically significant according to Welch’s t-test,
   two-tailed. f. Mitochondrial branches. N = 30–45 cells, 3 independent
   experiments. Error bars reflect SEM; not statistically significant
   according to Welch’s t-test, two-tailed. g. Representative images of
   BMDMs-Y and BMDMs-O immunostained for TOM20. Scale bar at 10 µm. h.
   Cytosolic Ca^2+ Oscillations in BMDMs-Y (n = 88 cells) and BMDMs-O (n =
   87 cells). i. Cytosolic Ca^2+ Oscillations with ATP. j. Maximum
   cytosolic Ca^2+. N = 112 cells, 2 independent experiments. Whiskers
   represent the min to max values for each data set. Box represents 75^th
   and 25^th percentile. Line is at the median; p-value according to
   Welch’s t-test, two-tailed. k. Number of oscillations in individual
   cells. N = 112 cells, 2 independent experiments. Whiskers represent the
   min to max values for each data set. Box represents 75^th and 25^th
   percentile. Line is at the median; p<value according to Welch’s t-test,
   two-tailed. l. Oscillation length in individual cells. N = 112 cells, 2
   independent experiments. Whiskers represent the min to max values for
   each data set. Box represents 75^th and 25^th percentile. Line is at
   the median; p<0.0001 according to Welch’s t-test, two-tailed. m.
   Cytosolic Ca^2+ Oscillations with OxPAPC. n. Maximum cytosolic Ca^2+. N
   = 104 cells, 2 independent experiments Whiskers represent the min to
   max values for each data set. Box represents 75^th and 25^th
   percentile. Line is at the median; p-value according to Welch’s t-test,
   two-tailed. o. Number of oscillations in individual cells. N = 104
   cells, 2 independent experiments. Whiskers represent the min to max
   values for each data set. Box represents 75^th and 25^th percentile.
   Line is at the median; p<value according to Welch’s t-test, two-tailed.
   p. Oscillation length in individual cells. N = 104 cells, 2 independent
   experiments. Whiskers represent the min to max values for each data
   set. Line is at the median; p<0.0001 according to Welch’s t-test,
   two-tailed.

   [143]Source data

Amplified cytosolic Ca^2+ oscillations in BMDMs-O responding to zymosan

   We hypothesized that a reduction in mCa^2+ uptake would disrupt cCa^2+
   signaling, which is crucial for inflammatory signaling. We challenged
   BMDMs derived from young and old mice with zymosan, a fungal glucan
   wherein the glucose monomers are polymerized through β-1,3 glycosidic
   bonds. Zymosan is a potent stimulator of both Toll-like receptor 2
   (TLR2) and dectin-1 (CLEC7A) receptors on myeloid cells. The downstream
   activation of phospholipase C-gamma (PLC-γ) elicits a robust
   store-operated Ca^2+ entry (SOCE), which involves an initial release of
   endoplasmic reticulum (ER)-resident Ca^2+ stores, followed by more
   sustained entry of extracellular Ca^2+ through the ORAI channels.
   Inflammatory gene expression mediated by multiple transcription
   factors, especially NF-κB, is highly sensitive to cCa^2+
   oscillations^[144]19,[145]20. In response to zymosan, the amplitudes of
   the cCa^2+ oscillations in BMDMs-O were significantly elevated compared
   to BMDMs-Y; typical and representative traces from cells are shown
   (Fig. [146]2e and Extended Data Fig. [147]4h). A statistical comparison
   of the maximum Ca^2+ elevations achieved in each cell also shows that
   the BMDMs-O achieved significantly higher amplitudes (Fig. [148]2f). In
   these experiments, ionomycin, a Ca^2+ ionophore, was used as a positive
   control demonstrating that both cell populations were loaded
   equivalently with the Ca^2+ dye (FURA-2AM) and were thus capable of
   reporting higher and equivalent levels of Ca^2+. The overlaid traces of
   cCa^2+ from all cells are also shown (Extended Data Fig. [149]4h). The
   spatial distribution of these oscillations across the imaging field
   (containing many cells) is also informative but not captured by such a
   traditional display of Ca^2+ oscillations. For a deeper analysis of
   this aspect of Ca^2+ dynamics, we used CALIMA, an image analysis
   software specially designed to measure spatiotemporal aspects of Ca^2+
   oscillations^[150]53. The spatial distribution of Ca^2+ oscillations in
   representative image fields is shown (Fig. [151]2g) with the origin of
   each circle at the cellular location and the diameter proportional to
   the number of spikes originating from that location. The color spectrum
   of the circles denotes the time at which that location first reported a
   spike. For instance, in each field, the location of the reddish-brown
   circle reported a Ca^2+ spike earlier than the circles colored green
   and so on. These spatial maps clearly show that BMDMs-O exhibit a
   significantly higher number of Ca^2+ oscillations for each cell and
   they also start spiking sooner than BMDMs-Y. These are quantified (Fig.
   [152]2h), and the differences in oscillatory lengths are also shown
   (Fig. [153]2i). These data establish that Ca^2+ elevations are
   amplified in the BMDMs-O during inflammatory signaling. Our analysis on
   Ca^2+ dynamics in response to fungal pathogens is highly relevant to
   chronic low-grade inflammation attributed to dysregulated microbiome
   and ‘leaky gut’ observed in older populations^[154]54. However, we were
   curious if this may also pertain to other mechanisms of chronic
   low-grade inflammation. Two additional sources of low-grade
   inflammation in older populations are ATP release from dying cells and
   oxidative stress. We subjected BMDMs-O to analyses of cCa^2+ dynamics
   in response to ATP (Extended Data Fig. [155]4i–l) and oxidized PAPC
   (OxPAPC; Extended Data Fig. [156]4m–p). Although the nature of Ca^2+
   response to ATP is different from that to zymosan, the BMDMs-O showed
   dysregulated cCa^2+ responses with an increase in maximum amplitudes
   (Extended Data Fig. [157]4j) and oscillation lengths (Extended Data
   Fig. [158]4l). This was not the case for OxPAPC-triggered Ca^2+
   responses—we did not find any significant differences between BMDMs-Y
   and BMDMs-O. This dichotomy suggests that mCa^2+ uptake doesn’t play a
   major role in OxPAPC-triggered Ca^2+ elevations.

Inconsistent changes in gene expression of store-operated Ca^2+ entry
components

   Recent reports in mice have suggested ER Ca^2+ channels as important
   drivers of cellular aging and senescence^[159]55. Since mCa^2+ uptake
   is thought to occur in close juxtaposition to ER Ca^2+ release^[160]56,
   we analyzed the whole-blood GTEx transcriptome for age-associated
   changes in SOCE machinery. We observed inconsistent changes in the
   expression of SOCE components. There was an upregulation in ITPR3 but
   downregulation of ITPR1 (Fig. [161]3a). Additionally, we observed a
   modest decrease in STIM1 expression but no effect of age on ORAI1 or
   STIM2 expression (Fig. [162]3a). These observations do not support a
   clear role for changes in SOCE machinery. We also assessed composite
   SOCE responses in BMDMs-O and BMDMs-Y (Fig. [163]3b). In these
   experiments, macrophages were loaded with Fura-2-AM and placed in a
   0 mM Ca^2+ bath and stimulated with thapsigargin, a sarcoplasmic
   reticulum Ca^2+ ATPase (SERCA) pump inhibitor, to visualize ER Ca^2+
   release (first elevation in cCa^2+). This is followed by the addition
   of 2 mM extracellular Ca^2+ to observe the ORAI-dependent Ca^2+ influx
   (second elevation; Fig. [164]3b). Loss of mCa^2+ uptake is predicted to
   increase both the first cCa^2+ elevation caused by the release of Ca^2+
   from the ER and the second elevation caused by the entry of
   extracellular Ca^2+ into the cytosol. Quantification of the ER Ca^2+
   release and ORAI Ca^2+ entry revealed increased SOCE responses in
   BMDMs-O (Fig. [165]3c,d). Although reduced mCa^2+ uptake is a major
   component of amplified SOCE, additional factors like increased release
   from the ER cannot be ruled out (Fig. [166]3e). Regardless of the
   mechanistic intricacies, amplified cCa^2+ responses are a hallmark of
   BMDMs-O.

Fig. 3. Store-operated Ca^2+ entry responses in macrophages generated from
aged mice.

   [167]Fig. 3
   [168]Open in a new tab

   a, ITPR1, ITPR2, ITPR3, ORAI1, STIM1 and STIM2 gene counts for each
   sample in whole-blood GTEx database sorted by age. Error bars reflect
   the s.e.m.; P values were calculated using ordinary one-way ANOVA. b,
   Representative cCa^2+ oscillations in BMDMs-Y and BMDMs-O. Bold lines
   indicated the mean of individual traces. c, Quantification of ER Ca^2+.
   N = 150 cells, three independent experiments. Error bars represent the
   s.e.m.; P values were determined by one-way ANOVA. d, Quantification of
   ORAI Ca^2+. N = 150 cells, three independent experiments. Error bars
   represent the s.e.m.; P value determined by one-way ANOVA. e, Fold
   change expression at baseline for SOCE genes. N = 6 biological
   replicates, two independent experiments. Error bars represent the
   s.e.m.; P value determined by Welch’s t-test, two-tailed.

   [169]Source data

Mcu^−/− BMDMs-Y recapitulate the amplified cytosolic Ca^2+ signals seen in
BMDMs-O

   To establish that this effect on cCa^2+ signaling is because of reduced
   mCa^2+ uptake, we show that the effect can be robustly recapitulated in
   BMDMs derived from young mice (age range, 15–25 weeks) by simply
   deleting Mcu. As shown (Extended Data Fig. [170]5a), Mcu^−/− BMDMs,
   derived from Mcu^fl(Cx3cr1-cre) mice^[171]42 show a robust
   amplification of cCa^2+ oscillations in response to zymosan. The
   maximum Ca^2+ levels achieved by individual cells are shown in Extended
   Data Fig. [172]5b. Appropriate controls for long-term Ca^2+ imaging is
   shown in Extended Data Figs. [173]5c and [174]6d. The spatiotemporal
   analysis using CALIMA is shown in Extended Data Fig. [175]5e–g. This
   analysis shows that, as seen in old BMDMs, the Mcu^−/− BMDMs exhibit
   many more oscillations for each cell, and the length of the
   oscillations is also increased. Together, these data establish that the
   increased cCa^2+ signaling in the old BMDMs is a direct consequence of
   the reduced mCa^2+ uptake.

Extended Data Fig. 5. cCa^2+ dynamics in Mcu−/− BMDMs-Y.

   [176]Extended Data Fig. 5
   [177]Open in a new tab

   a. Cytosolic Ca^2+ Oscillations in wt and Mcu−/− BMDMs. b. Maximum
   cytosolic Ca^2+. N = 83 cells, 2 independent experiments. Error bars
   represent SEM; p<0.0001 according to Welch’s t-test, two-tailed. c.
   Maximum cytosolic Ca^2+. N = 83 cells, 2 independent experiments. Error
   bars represent SEM; p<0.0001(****), p≤0.001(***), p≤0.01(**), p≤0.05(*)
   determined by the Brown-Forsythe and Welch ANOVA test. d. Maximum
   cytosolic Ca^2+. N = 83 cells, 2 independent experiments. Error bars
   represent SEM of two independent experiments; p<0.0001(****),
   p≤0.001(***), p≤0.01(**), p≤0.05(*) determined by the Brown-Forsythe
   and Welch ANOVA test. e. CALIMA maps depicting spatiotemporal aspects
   of cytosolic Ca^2+ elevations. f. Average number of oscillations per
   cell, seen during zymosan stimulation. N = 83 cells, 2 independent
   experiments Error bars represent SEM; p<0.0001 according to Welch’s
   t-test, two-tailed. g. Mean of oscillation lengths during zymosan
   stimulation. N = 83 cells, 2 independent experiments Error bars
   represent SEM; p<0.0001 according to Welch’s t-test, two-tailed.

   [178]Source data

Extended Data Fig. 6. Analysis of NFkB translocation in response to C.
albicans.

   [179]Extended Data Fig. 6
   [180]Open in a new tab

   a. Representative images for data shown in Fig. [181]3g shown as single
   channels. b. Representative images from wt and Mcu−/− BMDMs, untreated
   or stimulated with C. albicans, for 30 or 60 minutes and immunostained
   for NFκB p65 subunit and nuclei (DAPI). The outline of cells was
   determined by bright field images. c. Nuclear to cytoplasmic ratios of
   the fluorescent intensity of NFκB for images shown in panel b. N =
   30–72 cells, 2 independent experiments. Whiskers represent the min to
   max values for each data set. Box represents 75^th and 25^th
   percentile. Line is at the median; p-value according to one-way ANOVA.
   d. Representative analysis of fluorescent intensity of p65 staining
   along a line drawn across the cytoplasm and nucleus, as determined by
   DAPI staining (shaded blue). e. Zymosan-induced IL-1b expression (qPCR)
   in indicated BMDMs. N = 3 biological replicates. Error bars represent
   SEM, p-value calculated by one-way ANOVA.

   [182]Source data

BMDMs-O and Mcu^−/− BMDMs-Y show signatures of senescence

   Aging is commonly associated with the presence of senescent cells and a
   long-standing hallmark of senescent cells is senescence-associated
   β-galactosidase (SA-β-gal) activity attributed to the lysosomal
   β-galactosidase in mammals^[183]57,[184]58. To determine if BMDMs-O
   exhibited signs of cellular senescence, we compared SA-β-gal staining
   in BMDMs-O and BMDMs-Y (Fig. [185]4a). In this method, cells were
   stained with X-gal and imaged for the presence of blue precipitates
   formed by the activity of SA-β-gal. These precipitates can be observed
   under a bright-field light microscope and appear as dark aggregates. By
   measuring hundreds of cells, we found that BMDMs-O showed significantly
   increased SA-β-gal staining (Fig. [186]4a). We reasoned that if the
   loss of mCa^2+ uptake contributes to this senescent signature, we would
   observe SA-β-gal activity in Mcu^−/− macrophages from young mice.
   Indeed, Mcu^−/− macrophages from young mice had robust SA-β-gal
   activity (Fig. [187]4b). These findings bolstered the hypothesis that
   loss of mCa^2+ uptake in macrophages renders them hyper-inflammatory
   and as facilitators of inflammaging.

Fig. 4. BMDMs-O and young Mcu^−/− macrophages show signatures of senescence.

   [188]Fig. 4
   [189]Open in a new tab

   a, SA-β-gal activity. Scale bar, 10 µm. N = 6 biological replicates. b,
   SA-β-gal activity. Scale bar, 10 µm. N = 8 biological replicates. In a
   and b, error bars reflect the s.e.m., and P values were calculated
   using Welch’s t-test, two-tailed.

   [190]Source data

Hyper-inflammatory responses in both wild-type BMDMs-O and Mcu^−/− BMDMs-Y

   We hypothesized that the abnormally increased cCa^2+ signaling in
   BMDMs-O would result in increased inflammatory output. Indeed, in
   response to zymosan, BMDMs-O expressed higher levels of
   pro-inflammatory cytokines interleukin (IL)-6 and IL-1β when compared
   to BMDMs-Y (Fig. [191]5a). NF-κB plays a crucial role in the
   transcription of these pro-inflammatory genes, and its activation is
   highly sensitive to Ca^2+ signaling^[192]19,[193]59. We measured NF-κB
   translocation (p65) in BMDMs-Y and BMDMs-O stimulated with zymosan and
   found that, in accordance with our model, the translocation of NF-κB
   was significantly enhanced in the BMDMs-O (Fig. [194]5b and Extended
   Data Fig. [195]6a). The quantification of the ratio of nuclear to
   cytoplasmic NF-κB shows that significantly more NF-κB translocated to
   the nuclei of BMDMs-O (Fig. [196]5c). These data show that macrophages
   from the older mice are hyper-inflammatory in response to zymosan.
   Consistently, we found that Mcu^−/− macrophages from young mice also
   exhibit a hyper-inflammatory response to zymosan stimulation,
   establishing a model wherein reduction in mCa^2+ uptake increases
   inflammatory output through amplified cCa^2+ signaling (Fig. [197]5d).
   Predictably, the Mcu^−/− BMDMs-Y showed significantly increased
   expression of both IL-1β and IL-6 (Fig. [198]5e). Note that when
   ionomycin was added to artificially increase cCa^2+, even wild-type
   (WT) BMDMs-Y increased the expression of IL-1β, highlighting the
   sensitivity of the macrophage inflammatory response to cCa^2+. A
   similar effect was observed for IL-6, but there was a key
   difference—while ionomycin increased the expression of IL-6 in WT
   cells, it also decreased the expression of IL-6 in Mcu^−/− BMDMs. A
   possible reason for this is that unlike the oscillatory effects caused
   by reduced mCa^2+ uptake, ionomycin causes a global and sustained
   elevation of Ca^2+. In Mcu^−/− BMDMs, this elevation is unbuffered by
   mCa^2+ uptake, and this may inhibit other regulatory elements of IL-6
   transcription. Nevertheless, the upregulation of both IL-1β and IL-6 is
   highly dependent on Ca^2+ signaling. BAPTA-AM, a cell permeable,
   high-affinity Ca^2+ chelator that prevents the elevation of cCa^2+
   during zymosan-triggered inflammatory signaling completely abrogated
   the expression of IL-1β and IL-6 (Fig. [199]5f). NF-κB translocation
   was also found to be enhanced in Mcu^−/− BMDMs-Y (Fig. [200]5g).
   Quantification of the nuclear/cytoplasmic ratio revealed an increase in
   NF-κB activation in Mcu^−/− BMDMs-Y (Fig. [201]5h). Representative line
   intensity plots across the nucleus and cytosol are shown (Fig.
   [202]5i). The increased activation of NF-κB in Mcu^−/− macrophages was
   also seen when they were stimulated with the fungal pathogen Candida
   albicans (Extended Data Fig. [203]6b), but the translocation kinetics
   were slower in comparison to zymosan stimulation (Extended Data Fig.
   [204]6c). Representative line intensity plots are shown (Extended Data
   Fig. [205]6d). The inflammatory output, as measured by IL1B gene
   expression, of Mcu^−/− BMDMs was higher than that of BMDMs-O because
   mCa^2+ uptake was almost completely abrogated in Mcu^−/− BMDMs, while
   it was reduced in BMDMs-O (Extended Data Fig. [206]6e). During the
   macrophage response to zymosan, the influx of extracellular Ca^2+ into
   the cytosol depends predominantly on Orai channels. Predictably,
   treating the Mcu^−/− BMDMs with an inhibitor of Orai, the main conduit
   of SOCE, greatly blunted the gene expression of both IL-1β and IL-6
   (Extended Data Fig. [207]7a). BTP2 also blunted the nuclear
   translocation of NF-κB (Extended Data Fig. [208]7b). However, besides
   buffering cCa^2+ elevations, mCa^2+ uptake also regulates mitochondrial
   metabolism, primarily by regulating the activity of pyruvate
   dehydrogenase (PDH) complex and the TCA cycle^[209]60. In principle,
   the changes in mitochondrial metabolism in the Mcu^−/− BMDMs could
   exert an added effect on macrophage inflammatory output. Although this
   possibility cannot be completely ruled out, the following evidence
   suggests that changes in mitochondrial metabolism play a minimal role
   in the immediate regulation of inflammatory outputs in Mcu^−/− BMDMs.
   The influx of Ca^2+ into the mitochondrial matrix regulates the TCA
   cycle through the activation of PDH complex. The PDH complex is
   activated through dephosphorylation by the Ca^2+-activated PDH
   phosphatase (PDP)^[210]61 and abrogation of mCa^2+ uptake prevents the
   dephosphorylation (and activation) of PDH. We reasoned that treating
   the cells with AZD7545, an inhibitor of the PDH kinase, would counter
   the lack of PDP activity and restore PDH activity in a
   Ca^2+-independent manner. However, treating the Mcu^−/− BMDMs with
   AZD7545 did not reduce the gene expression of IL-6 and only modestly
   reduced IL-1β (Extended Data Fig. [211]7c). These results support the
   model wherein amplified Ca^2+ signaling in the cytosol is the main
   driver of the increased inflammatory output of Mcu^−/− cells. Although
   the role of mitochondrial metabolism in regulating this process is not
   supported by the available data, it cannot be ruled out.

Fig. 5. Mitochondrial Ca^2+ uptake buffers cytosolic Ca^2+ to control
inflammatory output in macrophages.

   [212]Fig. 5
   [213]Open in a new tab

   a, IL-6 and IL-1β mRNA expression from indicated BMDMs. Representative
   experiment, from two independent experiments. Error bars represent the
   s.e.m.; P < 0.0001 according to one-way ANOVA. b, Representative images
   from indicated BMDMs stimulated with zymosan for 30 and 60 min. Magenta
   shows immunostaining of NF-κB p65 subunit; cyan shows DAPI staining of
   nuclei. c, Nuclear to cytoplasmic ratios of the fluorescence intensity
   of NF-κB. N = 45 cells, three independent experiments. Bars reflect
   means of ratios; P values were determined by one-way ANOVA. d, Model
   depicting how mCa^2+ uptake affects cCa^2+ and inflammatory gene
   expression in response to zymosan. e, Effect of ionomycin on IL-1β and
   IL-6 mRNA expression in WT and Mcu^−/− macrophages. Representative
   experiment, from two independent experiments. Error bars represent the
   s.e.m.; P values were determined by two-way ANOVA. f, Effect of
   BAPTA-AM on IL-1β and IL-6 expression in WT and Mcu^−/− macrophages.
   Representative experiment, from two independent experiments. Error bars
   represent the s.e.m.; P value determined by two-way ANOVA. g,
   Representative images from WT and Mcu^−/− macrophages, untreated or
   stimulated with zymosan for 30 min and immunostained for NF-κB p65
   subunit and nuclei (DAPI). h, Nuclear to cytoplasmic ratios of the
   fluorescence intensity of NF-κB. N = 25 cells, from three independent
   experiments. Whiskers represent the minimum to maximum values for each
   dataset. The box represents the 75th and 25th percentiles. The line is
   the median; P < 0.0001 according to one-way ANOVA. i, Representative
   analysis of fluorescence intensity of p65 staining along a line drawn
   across the cytoplasm and nucleus (DAPI staining), which is shaded blue.

   [214]Source data

Extended Data Fig. 7. Effects of BTP2 and AZD7545 on inflammatory gene
expression and analysis of macrophage activation.

   [215]Extended Data Fig. 7
   [216]Open in a new tab

   a. Fold change expression of IL-1β and IL-6. N = 3 biological
   replicates. Error bars represent SEM; p-value determined by one-way
   ANOVA. b. Nuclear NFκB. N = 36–50 cells, 3 independent experiments.
   Error bars represent SEM; p-value determined by one-way ANOVA. c. Fold
   change expression of IL-1β and IL-6. N = 3 biological replicates. Error
   bars represent SEM; p-value determined by one-way ANOVA. d. Assessment
   of M1 polarization in wt and Mcu−/− BMDMs. N = 6 biological replicates.
   Error bars represent SEM; p-value determined by one-way ANOVA. e.
   Assessment of M2 polarization in wt and Mcu−/− BMDMs. N = 6 biological
   replicates. Error bars represent SEM; p-value determined by one-way
   ANOVA.

   [217]Source data

Abnormal functional polarization of Mcu^−/− macrophages

   Previous reports on the role of mCa^2+ on macrophage polarization have
   shown links between mCa^2+ and macrophage polarization. Mcu inhibition
   is linked to an impaired M2 polarization^[218]44,[219]62, while
   deletion of Mcub is linked to enhanced M1 polarization and impaired M2
   polarization^[220]43. The M1–M2 polarization is not universally
   accepted as being physiologically meaningful because macrophages
   achieve a spectrum of functional states in vivo. Nevertheless, we
   performed M1 and M2 polarization studies in Mcu^−/− macrophages
   (Extended Data Fig. [221]7d,e). M1 polarized Mcu^−/− macrophages showed
   enhanced expression of NF-κB target gene with unexpected expression of
   Arg1. Interestingly, M2 polarized Mcu^−/− macrophages also showed
   enhanced expression of M2 markers. However, such polarization
   experiments are performed over 24 h and the outputs are an integrated
   result of many secondary and tertiary signaling cascades in
   macrophages.

Mcu^−/− bone marrow-derived macrophages also show increased NLRP3
inflammasome activation

   Next, we evaluated if the increased inflammatory gene expression caused
   by decreased mCa^2+ uptake also increases inflammasome activation. WT
   and Mcu^−/− macrophages from young mice were stimulated with zymosan
   for 3 h before the addition of nigericin (5 µM) for 1 h to activate the
   NLRP3 inflammasome. Mcu^−/− macrophages released significantly more
   IL-1β (Fig. [222]6a) and lactate dehydrogenase (LDH; Fig. [223]6b), and
   this effect was also seen when the macrophages were first stimulated
   with lipopolysaccharide (LPS; Fig. [224]6c,d). We wondered if the
   assembly of the NLRP3 inflammasome is also accentuated in Mcu^−/−
   macrophages. Assembly of NLRP3 inflammasome can be visualized through
   immunofluorescence microscopy of ASC speck formation^[225]63–[226]67.
   We did not see any significant difference in ASC speck formation in
   Mcu^−/− BMDMs (Fig. [227]6e,f) indicating that decreased mCa^2+ uptake,
   and concomitantly increased cCa^2+ signaling, does not have a major
   impact on the assembly of NLRP3 inflammasome. Activation of the NLRP3
   inflammasome results in the proteolytic cleavage and activation of
   caspase-1 (CASP1). Activated CASP1 catalyzes the proteolytic processing
   of pro-IL-1β to its secreted form IL-1β. CASP1 also cleaves monomeric
   gasdermin D (GSDMD), thus catalyzing their oligomerization into a large
   multimeric gasdermin pore in the plasma membrane. The release of many
   potent pro-inflammatory mediators, including IL-1β and IL-18, is
   mediated through this large GSDMD pore. Overall, this process results
   in a highly inflammatory form of cell death called pyroptosis. We
   assessed the cleavage of CASP1 and GSDMD in NLRP3 activated
   macrophages. Notably, we found that the cleavage of both CASP1 and
   GSDMD was significantly increased in Mcu^−/− macrophages—in both cell
   pellets (Fig. [228]6g) and in supernatants (Fig. [229]6h). These
   findings indicate that while decreased mCa^2+ uptake does not affect
   NLRP3 assembly, it does have a significant impact on the downstream
   processing of CASP1 and GSDMD. Finally, we evaluated whether deletion
   of Mcu in the myeloid cells would manifest a hyper-inflammatory
   response in vivo. Previous reports have shown that long exposures to
   fungal beta-glucans can activate the NLRP3 inflammasome in
   macrophages^[230]68. In a model of zymosan-induced peritonitis, mice
   lacking Mcu in the myeloid cells, the Mcu(M)^−/− mice, exhibited
   significantly worse clinical scores, and increased levels of IL-1β and
   tumor necrosis factor (TNF) in the peritoneal cavity (Extended Data
   Fig. [231]8a–d). However, we did not see increased levels of other
   pro-inflammatory cytokines (IL-1α, IL-6 and IFN-γ) that we measured in
   this model.

Fig. 6. Mcu^−/− macrophages display increased inflammasome activation and
output.

   [232]Fig. 6
   [233]Open in a new tab

   a, IL-1β enzyme-linked immunosorbent assay (ELISA) of cell supernatants
   collected from WT and Mcu^−/− macrophages. Cells were stimulated with
   zymosan for 3 h followed by nigericin (5 µM) overnight. N = 3
   biological replicates. Error bars represent the s.e.m.; P values were
   determined by one-way ANOVA. b, LDH levels in cell supernatants were
   collected from WT and Mcu^−/− macrophages. N = 6 biological replicates.
   Error bars represent the s.e.m.; P < 0.0001 determined by one-way
   ANOVA. c, IL-1β ELISA of cell supernatants collected from WT and
   Mcu^−/− macrophages. Cells were stimulated with LPS for 3 h followed by
   nigericin (5 µM) overnight. N = 2 biological replicates. Error bars
   represent the s.e.m.; P values were determined by one-way ANOVA. d, LDH
   levels in cell supernatants collected from WT and Mcu^−/− macrophages.
   N = 6 biological replicates. Error bars represent the s.e.m.;
   P < 0.0001 determined by one-way ANOVA. e, Representative images of
   Mcu^−/− and WT macrophages immunostained for ASC and nuclei (DAPI).
   Cells were stimulated with zymosan for 3 h followed by nigericin (5 µM)
   for 1 h. f, Quantification of ASC specks, average number of specks
   counted per cell, for WT and Mcu^−/− macrophages. N = 3 biological
   replicates. Error bars represent the s.e.m.; no significance was
   determined by the one-way ANOVA. g, Western blot analysis of cell
   lysates from WT and Mcu^−/− macrophages stimulated with zymosan for 3 h
   followed by nigericin (5 µM) for 1 h. Cell lysates were immunoblotted
   for caspase-1 (p20), GSDMD and GAPDH. h, Western blot analysis of
   supernatants corresponding to samples shown in g. N = 1 representative
   replicate, from two independent experiments. Cell supernatants were
   immunoblotted for caspase-1 (p20), GSDMD and GAPDH.

   [234]Source data

Extended Data Fig. 8. Zymosan induced peritonitis in mice wherein Mcu is
deleted selectively in myeloid cells (Mcu(M)−/− mice).

   [235]Extended Data Fig. 8
   [236]Open in a new tab

   a. Percent weight change for wt and Mcu(M)−/− mice in a model of
   Zymosan Induced Peritonitis. N = 9–10 mice. b. Clinical Scores for wt
   and Mcu(M)−/− mice in a model of Zymosan Induced Peritonitis. N = 9–10
   mice. Error bars represent SEM; p≤0.05(*) determined by the one-way
   ANOVA. c. ELISA determined serum cytokines in wt and Mcu(M)−/− mice, at
   24 hours, in a model of Zymosan Induced Peritonitis. N = 5 mice. Error
   bars represent SEM; p<0.0001 according to Welch’s t-test, two-tailed.
   d. Peritoneal lavage cytokines at 24 hours. N = 4–5 mice. Error bars
   represent SEM of two independent experiments: p-values according to
   Welch’s t-test, two-tailed.

   [237]Source data

Diminished mitochondrial Ca^2+ uptake increases inflammatory output

   To develop a systems-level picture of how mCa^2+ uptake affects the
   inflammatory response, we performed RNA-seq analysis on WT, Mcu^−/− and
   BAPTA-AM loaded macrophages (all derived from young mice), before and
   after zymosan stimulation. In brief, the experiment was designed to
   reveal the Ca^2+-sensitive genes that are dysregulated when mCa^2+
   uptake is diminished. Note that BAPTA-AM loading will affect all
   Ca^2+-sensitive genes by ‘clamping’ intracellular Ca^2+ elevations to
   near resting levels (<100 nM). We were especially interested in groups
   of genes that are relatively upregulated in Mcu^−/− macrophages and
   downregulated in BAPTA-AM loaded cells. As expected, a volcano plot
   revealed that many inflammatory genes were significantly upregulated in
   zymosan-stimulated Mcu^−/− macrophages when compared to their WT
   counterparts (Fig. [238]7a). Conversely, BAPTA-AM loading broadly
   decreased the expression of inflammatory gene transcription (Fig.
   [239]7b). GSEA pathway analysis revealed the key pathways that follow
   this pattern of regulation in macrophages, that is, upregulated when
   mCa^2+ is diminished (cCa^2+ signaling is enhanced) and downregulated
   when all Ca^2+ signaling is prevented by BAPTA-AM (Fig. [240]7c). Genes
   involved in inflammatory responses and those involved in the
   overlapping TNF–NF-κB pathway showed this pattern most clearly. Using
   normalized counts, we showed significantly increased expression of
   Il1b, Il1a, Il6, Nlrp3, Cxcl9 and Clec5a when mCa^2+ uptake was
   diminished during an inflammatory response to zymosan (Fig. [241]7d).
   In total, we identified 668 genes that are regulated by mCa^2+ uptake
   in zymosan-stimulated macrophages (Fig. [242]7e). The analysis so far
   has focused on gene expression changes in response to a potent
   inflammatory stimulus (zymosan). We checked if abrogation of mCa^2+
   uptake in Mcu^−/− macrophages upregulates inflammatory genes at
   baseline—without any overt exposure to an inflammatory stimulus.
   Surprisingly, although the expression levels were low in quiescent
   macrophages, we observed a clear upregulation of inflammatory response
   genes in unstimulated Mcu^−/− macrophages (Fig. [243]7g,h). Similar to
   zymosan stimulation, GSEA pathway analysis revealed a significant
   enrichment of inflammatory pathways in Mcu^−/− macrophages at baseline
   when compared to WT controls (Extended Data Fig. [244]9a,b). Within the
   inflammatory pathway genes, Cxcl10, Il6 and Il12b were significantly
   elevated (Fig. [245]7i). Together, these data show that diminished
   mCa^2+ uptake drives low-grade inflammation in the absence of overt
   inflammatory stimuli and promotes a hyper-inflammatory response when
   the macrophages are exposed to inflammatory stimuli.

Fig. 7. RNA-seq analysis of Mcu^−/− and BAPTA-AM-loaded BMDMs reveals
transcripts sensitive to mCa^2+ uptake.

   [246]Fig. 7
   [247]Open in a new tab

   a, Volcano plot showing increased expression of inflammatory genes in
   Mcu^−/− BMDMs, compared to WT BMDMs, treated with zymosan for 3 h. Data
   normalization, dispersion estimates and model fitting (negative
   binomial) were carried out with the DESeq function (DESeq2 R package).
   Wald statistics were used for the significance tests. b, Volcano plot
   showing reduced expression of inflammatory genes in BAPTA-AM loaded
   BMDMs, when compared to unloaded BMDMs, treated with zymosan for 3 h.
   Data normalization, dispersion estimates and model fitting (negative
   binomial) were carried out with DESeq. Wald statistics were used for
   the significance tests. c, GSEA pathway analysis of Mcu^−/−, BAPTA-AM
   loaded (WT) and unloaded WT BMDMs stimulated with zymosan for 3 h. MΦ,
   macrophage. d, Normalized counts for representative genes in the GSEA
   hallmark inflammatory response pathway. N = 3–5 biological replicates.
   Error bars represent the s.e.m.; P values were determined by one-way
   ANOVA. e, Schematic showing gene transcripts used for BART analysis. f,
   BART analysis of 668 mCa^2+-sensitive genes identified in a.
   Transcription factor (TF) rank was plotted against the −log(P value)
   for each identified transcription factor. g, Volcano plot showing gene
   expression of the GSEA inflammatory response pathway in unstimulated
   Mcu^−/− macrophages, compared to their WT counterparts. Data
   normalization, dispersion estimates and model fitting (negative
   binomial) were carried out with DESeq. Wald statistics were used for
   the significance tests. h, Volcano plot showing expression of genes of
   the GSEA TNF–NF-κB pathway in unstimulated Mcu^−/− macrophages,
   compared to their WT counterparts. Data normalization, dispersion
   estimates and model fitting (negative binomial) were carried out with
   DESeq. Wald statistics were used for the significance tests. i,
   Normalized counts for representative genes in the GSEA hallmark
   inflammatory response pathway. N = 4 biological replicates. Error bars
   represent the s.e.m.; P value was determined by Welch’s t-test,
   two-tailed.

   [248]Source data

Extended Data Fig. 9. RNA-seq analysis of Mcu−/− macrophages and analysis of
IRF3 translocation.

   [249]Extended Data Fig. 9
   [250]Open in a new tab

   a. GSEA hallmark gene set enrichment analysis based on DSeq2 analysis
   of Mcu−/− versus wt BMDMs at baseline (untreated). b. Gene set
   enrichment analysis of GSEA pathways that are differentially expressed
   in Mcu−/− versus wt BMDMs at baseline (unstimulated). Enrichment scores
   (ES) are plotted on y-axis and genes ranked in ordered dataset are
   plotted in x-axis. c. Representative images from wt and Mcu−/−
   macrophages, untreated or stimulated with zymosan (30 min) and
   immunostained for IRF3 and nuclei (DAPI). Outline of cells was
   determined by bright field image. d. Quantified nuclear to cytoplasmic
   ratios of IRF3 fluorescence intensities in wt and Mcu−/− macrophages,
   unstimulated and zymosan-stimulated. N = 35–50 cells, 3 independent
   experiments. Box and whisker plot represents the min to max values for
   each data set. Line is at the median; p value calculated by one-way
   ANOVA test (found insignificant). e. Heatmaps show comparison of gene
   expression changes in the TNFα signaling via NFκB pathway from human
   and mice. Venn-diagram indicates similar genes upregulated in Human
   60–69 compared to young Mcu−/− at baseline and following zymosan
   stimulation.

   [251]Source data

Analysis of Ca^2+-sensitive inflammatory gene expression

   We applied the binding analysis for regulation of transcription (BART)
   analysis^[252]69 to predict transcriptional regulators of the 668
   mCa^2+-sensitive genes we identified (Fig. [253]7e). This analysis
   complemented the ex vivo experiments by implicating the RelA family,
   which includes NF-κB, as being the responsible transcription factors
   (Fig. [254]7f). However, this analysis also suggested that
   transcription by interferon regulatory transcription factor (IRF)
   family proteins IRF1 and IRF3 is regulated by mCa^2+ uptake; however,
   unlike NF-κB, we did not find any enhancement of IRF3 translocation in
   Mcu^−/− cells (Extended Data Fig. [255]9c,d). It is, however, possible
   that Ca^2+ signaling regulates an ancillary process of IRF3-mediated
   gene transcription. When we compare these datasets for similarities
   across species, we can identify 22 genes associated with inflammaging
   and reduced mCa^2+ uptake in macrophages (Extended Data Fig. [256]9e).

Mcu knockdown in human macrophages increases inflammatory output

   As illustrated (Extended Data Fig. [257]10a), we differentiated human
   monocyte-derived macrophages (HMDMs) by first enriching the human
   monocytes from donor buffy coats and then culturing them for 7 d in
   growth medium supplemented with macrophage colony-stimulating factor.
   Flow cytometry analysis confirmed proper differentiation of the HMDMs.
   The HMDMs exhibited high-density staining of the macrophage markers
   CXCL10 and CD86, which were absent on undifferentiated monocytes on day
   0 (Extended Data Fig. [258]10b,c). The short interfering RNA
   (siRNA)-mediated knockdown of MCU successfully reduced the mRNA levels
   of MCU, as measured by quantitative PCR with reverse transcription
   (RT–qPCR) analysis of MCU exon 3 and exon 6 (Extended Data Fig.
   [259]10d). Comparison of the mCa^2+ uptake capacity clearly showed a
   robust uptake in HMDMs transfected with scrambled siRNA control
   (siNT-HMDMs) and a significantly diminished mCa^2+ uptake in HMDMs
   transfected with MCU siRNA (siMCU-HMDMs; Fig. [260]8a). When stimulated
   with zymosan, the control siNT-HMDMs displayed Ca^2+ oscillations
   throughout the 30 min of imaging. But similar to BMDMs-O and Mcu^−/−
   BMDMs, the siMCU-HMDMs showed a significant increase in both the
   frequency and amplitude of the Ca^2+ oscillations (Fig. [261]8b,c). The
   spatiotemporal analysis of the Ca^2+ oscillations also revealed a
   similarity to mouse WT BMDMs-O and Mcu^−/− BMDMs-Y. The siMCU-HMDMs
   exhibited significantly more Ca^2+ spikes on an individual basis (Fig.
   [262]8d–f). Then, we checked the inflammatory response in siMCU-HMDMs
   and found that the expression levels of pro-inflammatory cytokines
   IL-1β, IL-6 and TNF were significantly higher when compared to
   siNT-HMDMs (Fig. [263]8g). Similar results were seen with LPS
   stimulation (Extended Data Fig. [264]10e). These results show that the
   sensitivity of the inflammatory response to mCa^2+ uptake is conserved
   and can be demonstrated readily in human macrophages.

Extended Data Fig. 10. Validation of HMDMs and analysis of LPS response in
HMDMs.

   [265]Extended Data Fig. 10
   [266]Open in a new tab

   a. Schematic for isolation and differentiation of HMDMs from human
   buffy coats. b. Flow cytometry-based validation of enriched human
   monocytes and differentiated HMDMs. c. Upregulation of CXCL10 and CD86
   in HMDMs after differentiation from monocytes. d. MCU mRNA levels in
   siNT and siMCU-transfected HMDMs. N = 3 biological replicates. Error
   bars represent SEM; p values determined by unpaired t-test. e. Fold
   changes in the gene expression of IL-1β, IL-6, TNFα, and IL-10 mRNA. N
   = 3 biological replicates. Error bars represent SEM; p-values
   determined by the one-way ANOVA.

   [267]Source data

Fig. 8. siRNA-mediated depletion of MCU in human monocyte-derived macrophages
renders them hyper-sensitive to inflammatory stimuli.

   [268]Fig. 8
   [269]Open in a new tab

   a, Representative trace for a mCa^2+ uptake in permeabilized siNT and
   siMCU-transfected HMDMs. Right, quantification of mCa^2+ uptake.
   N = 5–8 biological replicates. Error bars represent the s.e.m.;
   P = 0.0012 determined by t-test. b, cCa^2+ oscillations in siNT and
   siMCU-transfected HMDMs. ΔF/F[0] values for Fura-2-AM loaded
   macrophages were plotted. Images were taken every 3 s. c, Maximum
   cCa^2+. N = 88–98 cells, three independent experiments. Whiskers
   represent the minimum to maximum values for each dataset. The box
   represents the 75th and 25th percentiles. The line is the median;
   P < 0.0001 according to Welch’s t-test, two-tailed. d, CALIMA maps
   depicting spatiotemporal aspects of cCa^2+ elevations. e, Oscillation
   frequency was determined for individual cells. N = 88–98 cells, three
   independent experiments. Whiskers represent the minimum to maximum
   values for each dataset. The box represents the 75th and 25th
   percentiles. The line is at the median; P < 0.0001 according to Welch’s
   t-test, two-tailed. f, Oscillation length was determined for individual
   cells. N = 88–98 cells, three independent experiments. Whiskers
   represent the minimum to maximum values for each dataset. The box
   represents the 75th and 25th percentiles. The line is the median;
   P < 0.0001 according to Welch’s t-test, two-tailed. g, Gene expression
   of IL-1β, IL-6, TNF and IL-10 mRNA. N = 3 biological replicates. Error
   bars represent the s.e.m.; P values were determined by one-way ANOVA.

   [270]Source data

Discussion

   In this study, we report a surprising discovery that mCa^2+ uptake
   capacity in macrophages drops significantly with age. This amplifies
   cCa^2+ signaling and promotes NF-κB activation, rendering the
   macrophages prone to chronic low-grade inflammatory output at baseline
   and hyper-inflammatory when stimulated. Although mitochondrial
   dysfunction has long been a suspected driver of aging, our study
   pinpoints the MCU complex as a keystone molecular apparatus that links
   age-related changes in mitochondrial physiology to macrophage-mediated
   inflammation.

   Gene expression analyses of human blood revealed clear signs of chronic
   age-associated inflammation, supporting the idea that blood
   transcriptomics can be used to monitor biomarkers of age-related
   low-grade inflammation. Both chronic low-grade inflammation and
   mitochondrial dysfunction are known hallmarks of aging, but mechanistic
   links between these two processes have not been defined with clear
   links to human biology^[271]70,[272]71. For example, defective
   mitophagy in Prkn^−/− mice may contribute to inflammaging by shedding
   mitochondrial DNA as an inflammatory stimulus in senescent
   cells^[273]71. Although a progressive age-associated decline in
   mitophagy is not evident in human myeloid cells, if one supposes that
   there is a steady age-associated shedding of inflammatory mediators
   from other senescent cells, our findings predict that the decreased
   mCa^2+-uptake capacity will render the macrophages hyper-responsive to
   such inflammatory stimuli from senescent cells and thereby drive
   inflammaging. A recent study performed a comprehensive analysis of
   mitochondrial phenotypes in purified human cell types and mixtures but
   omitted mCa^2+ uptake as a marker of mitochondrial fitness^[274]72.
   Interestingly, the authors found that their mitochondrial health index
   was most impaired in monocytes isolated from aged human donors.
   Although we chose to focus on macrophage-mediated inflammation, the
   broad outlines of the mechanistic model are likely applicable to other
   myeloid cells such as neutrophils and mast cells too, and that is an
   important line for our future investigations.

   Whether macrophages deficient in mCa^2+ uptake are hyper-inflammatory
   to all kinds of inflammatory stimuli is an outstanding question. For
   example, tissue-resident macrophages are constantly exposed to
   purinergic signals^[275]73 and our model predicts that in older mice
   and humans, the reduced Ca^2+-buffering capacity of mitochondria will
   render the myeloid cells hyper-responsive to such purinergic signals.
   In addition to increased inflammation, a reduction in mCa^2+ uptake
   also affects the ability of macrophages to phagocytose and kill
   pathogens, an immunological deficit known to be related to age.
   Analysis of genes that are especially sensitive to mCa^2+ uptake points
   to the RelA family as the key transcription factors involved in this
   process. Consistently, we show that the nuclear translocation of NF-κB,
   which is central to the inflammatory response, is enhanced when mCa^2+
   uptake is diminished. Notably, the loss of mCa^2+ uptake also results
   in hyperactivation of NFAT^[276]74, a transcription factor that is
   exquisitely sensitive to Ca^2+ signaling. But in contrast to NFAT
   regulation, where Ca^2+ is known to regulate NFAT nuclear translocation
   through the Ca^2+-activated phosphatase calcineurin^[277]75,[278]76,
   the precise mechanisms through which Ca^2+ regulates NF-κB
   translocation are not entirely clear.

   In a landmark study, Fieni et al. used direct patch-clamp recordings of
   mitoplasts to demonstrate that MCU current density varies greatly
   between tissues^[279]77. Remarkably, they found that MCU current
   densities in the cardiac mitoplasts from newborn mice were nearly five
   times larger than those found in the adult counterparts. In accordance
   with those findings, our analysis of the human gene expression data
   shows that expression of MCU in the heart reduces progressively with
   age. In addition to age-related changes in gene expression, other
   ancillary mechanisms, such as posttranslational modifications of the
   MCU complex, are likely to be very relevant to the overall regulation
   of mCa^2+ uptake. Although we have clearly demonstrated that reduced
   mCa^2+ uptake is an important component of this aging process, we
   cannot rule out other age-associated changes in the molecular machinery
   of Ca^2+ signaling. For instance, we have evidence that ITPR3
   expression increases with age. Interestingly, it was proposed recently
   that loss of Itpr2 leads to improved lifespan in mice^[280]55. Our
   future studies will focus on determining whether the ER–mitochondria
   contact sites in the macrophages are disrupted with age and on defining
   the regulatory mechanisms of the age-associated reduction in MCU
   transcription.

   A major paradox in the field of mCa^2+ signaling is that despite that
   its machinery is highly conserved in invertebrates and
   vertebrates^[281]78, and expressed ubiquitously in mammalian tissues,
   the deletion of Mcu, in the mixed background, yields smaller but viable
   mice^[282]79. These mice display moderate defects in skeletal muscle
   function^[283]79, but the overall phenotype is surprisingly mild for a
   process that is so well conserved and ubiquitous. The phenotype of
   global Mcu^−/− mice is likely confounding in this respect because when
   a gene is deleted during embryogenesis, there is often a developmental
   compensation (not necessarily in the same molecular function). It is
   now becoming increasingly clear that a major role for mCa^2+ signaling
   is tied to innate immune responses, the salience of which is largely
   masked in unchallenging conditions of a mouse vivarium. Recent studies
   establish that far from being a redundant Ca^2+-buffering system, this
   molecular apparatus, centered on MCU and its regulatory subunits, has a
   profound role in host defense and inflammatory processes. Ironically, a
   steady age-associated erosion of its activities not only dampens the
   innate immune responses to fungal pathogens^[284]42, but also drives
   chronic low-grade inflammation. Interestingly, MCU-mutant flies show
   reduced lifespan^[285]80 but analyses of MCU-null hemocytes, the cells
   that constitute the Drosophila innate immune system, were not carried
   out. In mammals, tissue-resident macrophages occupy specialized niches
   in all organ systems. Age-associated decrease in the mCa^2+-uptake
   capacity in these specialized tissue-resident macrophages may increase
   local inflammation and thereby have a major impact on organ physiology
   and homeostasis. An intriguing possibility is that resident macrophages
   of certain organ systems may be especially susceptible to such
   age-related changes. Our study sets the stage for many such research
   directions that may ultimately allow us to slow the onset and
   progression of many age-related diseases where chronic inflammation
   plays either a germinating or exacerbating role.

Methods

Mouse strains

   Male and female mice aged 15 to 25 weeks (young) and 80–90 weeks (old)
   were used for all experiments. C57BL/6 mice we purchased from Jackson
   Laboratories (000664) within indicated age ranges. For WT and
   Mcu(M)^−/− mouse experiments, mice aged 15–25 weeks were used.
   Mcu(M)^−/− mice are reported previously^[286]42. The mouse experiments
   carried out in this study were reviewed and approved by the University
   of Virginia IACUC under the active protocol 3916.

Cell lines and cell culture

   All cells were grown and maintained at 37 °C, 5% CO[2]. BMDMs were
   isolated and cultured as previously described^[287]81. In brief, bone
   marrow was extracted from mouse femur and tibia via centrifugation. The
   red blood cells were lysed with ACK lysis buffer and the remaining
   cells were counted and plated on petri dishes at a density of
   2–4 × 10^6 cells per plate in BMDM Media (RPMI 1640 + 10% FBS + 20%
   L929-conditioned media). Cells were differentiated for 7 d and medium
   was replaced every 3 d. For experiments, BMDMs were used between days
   9–14 after harvest.

GTEx and differential gene expression analysis

   GTEx Analysis V8, gene counts and metadata were downloaded from the
   GTEx portal ([288]https://gtexportal.org/)^[289]49 and analyzed using
   RStudio. Expression profile data were obtained for different tissues,
   binned into age groups and then subjected to differential gene
   expression analysis using DESeq2 (ref. ^[290]82) R package. PCA plots
   were generated using the plotPCA function. The differentially expressed
   genes were ranked based on the log[2] fold change and FDR-corrected P
   values. The ranked list was then used to perform pathway analysis using
   GSEA software^[291]83. For the analysis of genes associated with
   mitochondrial functions, the differentially expressed genes were
   uploaded to mitoXplorer1.0 (ref. ^[292]50) for pathway analysis.
   Comparative plots were generated for specified pathways and the log[2]
   fold change was plotted for individual genes.

Mitochondrial Ca^2+ uptake in permeabilized macrophages

   Ca^2+ uptake assay was adapted for macrophages from Wettmarshausen et
   al.^[293]84 as reported in Seegren et al.^[294]42. In brief, cells were
   washed two times in D-PBS (without Ca^2+ and Mg^2+) and resuspended in
   ICM buffer containing 120 mM KCl, 5 mM NaCl, 1 mM MgCl[2], 2 mM
   KH[2]PO[4], 20 mM HEPES, 5 mM succinate, 5 mM malate, 5 mM glutamate,
   500 nM thapsigargin and 0.1 µM calcium green-5N. Cells were immediately
   permeabilized with 35 µM digitonin for 5 min before recording on a
   FlexStation plate reader. Calcium green-5N fluorescence intensity was
   recorded every 2 s for the indicated time with injections of CaCl[2]
   (concentration indicated in figure legend) and 10 µM FCCP at indicated
   times.

Cytosolic Ca^2+ imaging using ratiometric Fura-2

   Ratiometric imaging of macrophages using Fura-2-AM was as described
   previously by Seegren et al.^[295]42. In brief, macrophages were
   incubated for 30 min with gentle agitation at room temperature (RT)
   with 5 μM Fura-2-AM, 0.02% of pluronic acid and 500 μM probenecid in
   Ringer solution (155 mM NaCl, 4.5 mM KCl, 2 mM CaCl[2], 1 mM MgCl[2],
   5 mM HEPES and 10 mM glucose, pH 7.4). Fura-2 emissions were collected
   at 510 nm and with 340/380 nm of excitation. Excitation was performed
   using a DG4 Illuminator (Sutter Instruments).

CALIMA analysis

   Images acquired from cCa^2+ imaging were uploaded into the
   CalciumImagingAnalyser from Radstake et al.^[296]53. Regions of
   interest were drawn manually over cells and processed for recorded cell
   activity. Spike detection parameters were set to the same values for
   each replicate and Excel sheets were exported for analysis in PRISM.

Bulk RNA-seq analysis

   On average we received 30 million paired-end sequences for each of the
   replicates. RNA-seq libraries were checked for their quality using the
   fastqc program
   ([297]http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The
   results from fastqc were aggregated using MultiQC software^[298]85. A
   program developed in-house was used for adaptor identification, and any
   contamination of adaptor sequence was removed with cutadapt
   ([299]https://cutadapt.readthedocs.io/en/stable/). Reads were then
   mapped with the ‘splice aware’ aligner ‘STAR’^[300]86, to the
   transcriptome and genome of mm10 genome build. The HTseq
   software^[301]87 was used to count aligned reads that map onto each
   gene. The count table was imported to R to perform differential gene
   expression analysis using the DESeq2 package^[302]82. Lowly expressed
   genes (genes expressed only in a few replicates and had low counts)
   were excluded from the analysis before identifying differentially
   expressed genes. Data normalization, dispersion estimates and model
   fitting (negative binomial) were carried out with the DESeq function.
   The differentially expressed genes were ranked based on the log[2]fold
   change and FDR-corrected P values. The ranked file was used to perform
   pathway analysis using GSEA software^[303]83. The enriched pathways
   were selected based on enrichment scores as well as normalized
   enrichment scores.

Binding analysis for regulation of transcription

   A gene list of 668 Ca^2+-sensitive genes was uploaded into the BART web
   interface developed and maintained by the C. Zang laboratory at the
   University of Virginia^[304]69. The software identified the most likely
   transcription factors regulating the input genes. The area under the
   curve and P values were exported and plotted.

Immunoblotting

For analysis of caspase-1 and GSDMD

   After treatment, the plates were centrifuged at 400g for 4 min and
   cell-free supernatants were collected. Cell lysates were prepared by
   directly adding 1× Laemmli sample buffer into the pellets and stored at
   −80 °C. At the day of electrophoresis, cell lysates were transferred
   into a 1.5-ml tube, sonicated and boiled at 95 °C for 5 min. Collected
   supernatants were cleared again by centrifugation at 400g for 5 min.
   Proteins in the supernatants were precipitated using 20%
   trichloroacetic acid, resuspended in 1× Laemmli sample buffer, and
   boiled at 95 °C for 5 min. Cell lysates and concentrated supernatants
   were run on a 12% homemade SDS–PAGE gel and transferred on to a 0.45-µm
   PVDF membrane (Millipore) using Towbin wet transfer buffer. After
   transfer, Ponceau S staining was performed to confirm equal loading of
   total proteins and the membrane was then blocked by 5% non-fat milk in
   TBST for 1 h at RT. Primary antibodies were diluted in TBST and
   incubated at 4 °C overnight. Horseradish peroxidase (HRP)-conjugated
   secondary antibodies were diluted in TBST at 1:10,000 and incubated for
   1 h at RT. Membrane was developed by adding Luminata Forte Western HRP
   substrate (Millipore, WBLUF0100) and imaged on a Bio-Rad ChemiDoc
   Imager. Primary antibodies used were mouse anti-mCasp1(p20) (AdipoGen,
   Casper-1, 1:1,000 dilution) and rabbit anti-mGSDMD (Abcam, ab209845;
   1:1,000 dilution). Secondary antibodies used were anti-mouse, HRP
   (Jackson, 115-035-003) and anti-rabbit, HRP (Jackson, 111-035-144).

Nuclear and cytoplasmic fractions

   The Thermo Scientific NE-PER Nuclear Cytoplasmic Extraction Reagent kit
   was used to generate nuclear and cytosolic fractions from cells
   following zymosan stimulation. Briefly, cells underwent reagent-based
   lysis using cytoplasmic extraction reagents I & II followed by
   centrifugation for 5 min at 16,000g to separate nuclei from cytosolic
   fractions. The nuclei were then lysed using the nuclear extraction
   reagent and centrifuged for 5 min at 16,000g. The resulting supernatant
   contained the nuclear extract and was used for subsequent western
   blotting.

Native gel electrophoresis of MCU complex

   The mitochondrial membrane proteins were extracted by incubating the
   isolated mitochondria with 1% digitonin on ice for 30 min. Samples were
   vortexed every few minutes. The mitochondrial extracts were then mixed
   at a 1:1 ratio with non-reducing sample loading buffer (62.5 mM
   Tris-HCl, pH 6.8, 40% (wt/vol) glycerol and 0.01% (wt/vol) bromophenol
   blue). Samples were resolved on 4–20% Mini-Protean TGX Stain-Free
   precast gels (Bio-Rad; 4568096) and run at 200 V for 5 h on ice. The
   gels were transferred onto a PVDF membrane using the Bio-Rad Turbo-blot
   system. Membranes were blocked with 5% milk for 30 min with gentle
   agitation before immunoblotting with primary antibody (α-MCU, clone
   D2Z3B, Cell Signaling 14997S and α-MICU1, clone D4P8Q, Cell Signaling
   12524) in Signal Boost Immunoreaction Enhancer (Calbiochem,
   407207-1KIT) at a 1:1,000 dilution. Staining with primary antibody was
   carried out overnight at 4 °C. Secondary anti-rabbit HRP was used in
   Signal Boost Immunoreaction enhancer for 2 h. The immunoblots were
   developed with SuperSignal West Pico PLUS Chemiluminescent Substrate
   (Thermo Fisher, 34580) 5 min before imaging.

qPCR

   For all qPCR experiments, macrophages were plated at ~80% confluency
   into tissue culture-treated plates and rested overnight. For
   inflammatory gene expression, the following day, BMDMs were stimulated
   with Zymosan A BioParticles (Thermo Fisher, Z2849) at two particles per
   cell. For pharmacological pretreatments, on the day of experiment,
   BMDMs were treated with inhibitors for 30 min before the addition of
   zymosan, as described above. Inhibitor information and concentrations
   used were: BAPTA-AM (Thermo Fisher, B6769; 10 µM), ionomycin (Cayman
   Chemicals, 10004974; 1 µM), BTP2 (Sigma, 203890-M; 10 µM), zegocractin
   (MedChemExpress, HY-101942; 1 µM) and AZD7545 (MedChemExpress,
   HY-16082; 1 µM). For M1 versus M2 polarization, BMDMs were treated
   either with IFN-γ (R&D Systems, 485-MI-100/CF; 100 ng ml^−1) and LPS
   (Invivogen, tlrl-eblps; 100 ng ml^−1; to induce polarization toward a
   M1 phenotype) or with IL-4 (R&D Systems, 404-ML-010; 20 ng ml^−1; to
   induce polarization toward a M2 phenotype). Total RNA was isolated from
   treated cells using RNeasy Plus Mini Kit. Following isolation, RNA
   concentration was determined using a NanoDrop 2000c spectrophotometer.
   RNA was converted to cDNA in a two-step reverse transcriptase process
   using the Promega Reverse Transcription Master Mix. Following cDNA
   synthesis, a Bio-Rad CFX Connect Real-Time system was used to perform
   qPCR reactions with SYBR Select Master Mix and 1 to 5 ng cDNA per well
   in a 96-well plate.

Immunofluorescence

   Cells were plated overnight on coverslips before experiments. BMDMs
   were stimulated with Zymosan A BioParticles (Thermo Fisher, Z2849) at
   two particles per cell for indicated times. Following treatments,
   coverslips were washed 3× in PBS to remove loose/non-adherent cells.
   Coverslips were fixed in 4% paraformaldehyde and 4% sucrose (30 min,
   RT). Coverslips were washed 3× in wash buffer (PBS with 0.05%
   Tween-20), blocked and permeabilized at RT for 1 h in B/P buffer (1%
   BSA, 0.1% Triton X-100 and 0.05% Tween-20 in PBS), and then incubated
   with primary antibody diluted in B/P buffer overnight at 4 °C.
   Coverslips were washed 3× in wash buffer, and incubated at RT with the
   appropriate secondary antibody in B/P buffer for 2 h, followed by 3×
   washes in wash buffer. Coverslips were mounted on glass slides (ProLong
   Gold Antifade; Thermo Fisher, [305]P36930), stored in a desiccation box
   at 4 °C until imaging. Confocal microscopy was performed on a Zeiss LSM
   880. Data were acquired with Zen Black and analyzed using ImageJ.
   Antibodies used for immunofluorescence were: anti-ASC/TMS1/PYCARD
   antibody (F-9): sc-271054, Santa Cruz; anti-NF-κB p65 (D14E12) XP
   rabbit monoclonal antibody 8242, Cell Signaling; anti-IRF-3 (D83B9)
   rabbit monoclonal antibody 4302, Cell Signaling.

Mitogenie

   To analyze mitochondrial morphology and other characteristics, images
   were cropped into individual cells and processed using a mitochondrial
   analysis workflow developed by the Kashatus laboratory^[306]52. Images
   were first input into the MitoCatcher application on the Mitogenie
   platform, generating binarized images of segmented mitochondrial
   networks. The MiA application on Mitogenie was used to analyze the
   images of the mitochondrial networks and produce quantitative
   measurements describing mitochondrial morphology.

LDH assay

   BMDMs were incubated for 3 h with LPS (Invivogen, tlrl-eblps; 100 ng
   ml^−1) or Zymosan A BioParticles (Thermo Fisher, Z2849; two particles
   per cell) at 37 °C and 5% CO[2]. After 3 h, cells were washed 3× with
   HBSS, resuspended in Ringer solution (155 mM NaCl, 4.5 mM KCl, 2 mM
   CaCl[2], 1 mM MgCl[2], 5 mM HEPES and 10 mM glucose, pH 7.4) with or
   without nigericin (Invivogen, tlrl-nig; 5 µM). After 24 h, LDH was
   measured in the supernatants using Pierce LDH Cytotoxicity Assay Kit
   (Thermo Fisher Scientific) according to the manufacturer’s
   instructions.

IL-1β ELISA

   BMDMs were incubated for 3 h with LPS (Invivogen, tlrl-eblps; 100 ng
   ml^−1) or Zymosan A BioParticles (Thermo Fisher, Z2849; two particles
   per cell) at 37 °C, 5% CO[2]. After 3 h, cells were washed 3× with
   HBSS, resuspended in Ringer solution (155 mM NaCl, 4.5 mM KCl, 2 mM
   CaCl[2], 1 mM MgCl[2], 5 mM HEPES, 10 mM glucose, pH 7.4) with or
   without nigericin (Invivogen, tlrl-nig; 5 µM). After 24 h, IL-1β was
   measured in the supernatants using ELISA MAX Standard Mouse IL-1β
   (BioLegend) according to the manufacturer’s instructions.

Zymosan-induced peritonitis

   Mcu(M)^−/− and WT mice were subjected to a model of zymosan-induced
   peritonitis^[307]88. In brief, mice were intraperitoneally injected
   with 55 mg per kg body weight Zymosan A and monitored for 24 h for
   clinical scores of conjunctivitis, lethargy, changes in hair coat and
   grimace pain to indicate symptoms of illness. Weight was monitored
   every 2 h and scoring was performed by a blinded member of the
   laboratory. Following 24 h, mice were euthanized. Blood and peritoneal
   lavage fluid were collected for Luminex analysis and cytokine
   detection.

Differentiation of human monocyte-derived macrophages

   Human monocytes were isolated from healthy donor buffy coats procured
   from the American Red Cross Biomedical Services (ARCBS), with consent
   from the volunteer donors for whole-blood collection and its use for
   research (Leukpacks/Whole Blood Clinical Study Protocol LP-2). The
   distribution of buffy coats to the laboratory of B.N.D. at University
   of Virginia was also reviewed and approved by the ARCBS Institutional
   Review Board under the active protocol 2016-030. Differentiation of
   HMDMs was performed using PromoCell, Serum-free and Zeno-free cell
   culture method. In brief, buffy coats were enriched for monocytes using
   RosetteSep Human Monocyte Enrichment Cocktail. Enriched monocytes were
   plated on six-well plates in monocyte attachment medium for 1 h in a 5%
   CO[2] and 37 °C incubator. Cells were washed three times with vigorous
   swirling in warm monocyte attachment medium to remove non-adherent
   cells. Cells were cultured for 7 d in Macrophage Generation Medium DXF
   with supplement mix to generate HMDMs. The siRNA knockdown of MCU was
   performed two times over 48 h using Lipofectamine 3000 with 10 nM
   siRNA. Antibodies used for flow cytometry were: FITC anti-human CD14
   antibody, BioLegend 325603; PE/Cyanine7 anti-human CD86 Antibody,
   BioLegend 305421; and PE anti-human CXCL10 (IP-10) antibody, BioLegend
   519503. Flow cytometry was performed on an Attune NxT with Attune NxT
   Software (v2.0+).

Statistics and reproducibility

   All data were analyzed using Excel (Microsoft) and Prism 8 (GraphPad)
   software. All datasets were subjected to ROUT outlier test and the data
   points with Q < 1% were considered outliers and removed. In bar graphs,
   data are presented as means with error bars reflecting the s.e.m. or as
   indicated in figure legends. Statistical significance (P < 0.05) was
   computed using one-way ANOVA, two-way ANOVA and Welch’s t-test
   (two-tailed), as indicated in figure legends. The sample size and
   representation of ‘n’ (mice, experimental repeats or cells) is
   indicated in figure legends. Sample size was determined by using
   GPower3.l software. For all other experiments (ex vivo and in vivo), no
   power analysis was used for sample sizes and replicates, but they were
   determined based on experimental experience. In the box plots, the
   whiskers represent minimum and maximum values, the box represents the
   75th and 25th percentiles and the horizontal line is the median. For
   zymosan-induced peritonitis, clinical scores were collected by
   laboratory personnel who were blinded to the genotype/condition of
   individual mice. All other experiments were not blinded but contained
   appropriate biological replication. The order of data collection was
   changed between experiments to avoid collection bias (for example, if
   the experiment was run as control, WT and then knockout for one
   experiment, the following experiment data was collected as knockout, WT
   and then control).

Reporting summary

   Further information on research design is available in the [308]Nature
   Portfolio Reporting Summary linked to this article.

Supplementary information

   [309]Supplementary Information Supplementary Fig. 1^ (14.5MB, pdf)

   Unprocessed blots and primer information.
   [310]Reporting Summary^ (2.5MB, pdf)

Acknowledgements