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Loss of Mir146b with aging contributes to inflammation and mitochondrial dysfunction in thioglycollate-elicited peritoneal macrophages

  1. Andrea Santeford
  2. Aaron Y Lee
  3. Abdoulaye Sene
  4. Lynn M Hassman
  5. Alexey A Sergushichev
  6. Ekaterina Loginicheva
  7. Maxim N Artyomov
  8. Philip A Ruzycki
  9. Rajendra S Apte  Is a corresponding author
  1. Department of Ophthalmology and Visual Sciences, Washington University in St. Louis School of Medicine, United States
  2. Department of Pathology and Immunology, Washington University in St. Louis School of Medicine, United States
  3. Department of Medicine, Washington University in St. Louis School of Medicine, United States
  4. Department of Developmental Biology, Washington University in St. Louis School of Medicine, United States
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Cite this article as: eLife 2021;10:e66703 doi: 10.7554/eLife.66703

Abstract

Macrophages undergo programmatic changes with age, leading to altered cytokine polarization and immune dysfunction, shifting these critical immune cells from protective sentinels to disease promoters. The molecular mechanisms underlying macrophage inflammaging are poorly understood. Using an unbiased RNA sequencing (RNA-seq) approach, we identified Mir146b as a microRNA whose expression progressively and unidirectionally declined with age in thioglycollate-elicited murine macrophages. Mir146b deficiency led to altered macrophage cytokine expression and reduced mitochondrial metabolic activity, two hallmarks of cellular aging. Single-cell RNA-seq identified patterns of altered inflammation and interferon gamma signaling in Mir146b-deficient macrophages. Identification of Mir146b as a potential regulator of macrophage aging provides novel insights into immune dysfunction associated with aging.

Introduction

Macrophages are innate immune cells that perform critical surveillance functions and phagocytose pathogens and cellular debris (Mosser and Edwards, 2008; Wang et al., 2019). Through surface-bound or secreted factors, they signal to other cells and regulate diverse processes such as angiogenesis, inflammation, and fibrosis. These global functional consequences of macrophage activity make these cells critical in regulating the molecular pathogenesis of cardiovascular disease, cancer, neurodegenerative disorders of the central nervous system, and blinding eye conditions (Apte et al., 2006; Kelly et al., 2007; Nakao et al., 2005; Moore and Tabas, 2011; Mammana et al., 2018). Macrophages undergo broad programmatic changes with aging that manifest as abnormal macrophage activation and polarization and are phenotypically labeled as immunosenescence or inflammaging (Xia et al., 2016; Lin et al., 2018; Sene and Apte, 2014). This age-associated shift in the macrophage phenotype from disease protective to disease promoting led us to hypothesize that alterations in the aged macrophage transcriptome may regulate this dysfunction.

Altered macrophage polarization and activation are associated with aging and drive molecular inflammation. Although this age-induced macrophage-mediated phenotype has been partially characterized, the current paradigm relies on an incomplete cytokine signature to determine whether macrophages mitigate or promote disease with little information about the altered regulatory networks that inform downstream effector function of aging macrophages (Locati et al., 2013; Nakamura et al., 2015). Over the past decade, numerous studies have described how microRNAs (miRNAs), short non-coding RNAs ~22 nucleotides long, regulate gene expression by either transcript degradation or translational repression (Bartel, 2009). Multiple laboratories have demonstrated that miRNAs control critical processes in macrophages, including cholesterol efflux, lipid metabolism, and polarization (Lin et al., 2018; Sene et al., 2013; Banerjee et al., 2013; Cai et al., 2012). We hypothesized that expression of specific macrophage miRNAs is altered with organismal aging and may drive the inflammaging process. We tested this by examining changes in miRNA expression by RNA sequencing (RNA-seq). Using this unbiased approach, we identified a potentially high-value miRNA (Mir146b-5p) that inversely correlates with thioglycollate-elicited macrophage (TGEM) host age. Here we demonstrate that expression of Mir146b progressively declines in the aging TGEM and is associated with significant mitochondrial dysfunction and abnormal macrophage activation and polarization, recapitulating the inflammaging phenotype.

Results

TGEM purity is unaffected by host age

Although miRNAs have been implicated in regulating age-associated gene expression, their role in directing gene expression patterns and function in aging macrophages is unclear. We hypothesized that altered transcriptional regulation by miRNAs contributed to age-associated programmatic alterations in macrophages and sought to examine these changes in non-coding RNA expression on a genome-wide scale. Thioglycollate injection induces a sterile inflammatory response in the mouse peritoneum, which elicits infiltration of monocytes from the blood. Lavage of the peritoneum several days post-injection allows for the collection of the activated TGEM population, consisting of both resident and recruited macrophages (Pavlou et al., 2017; Ghosn et al., 2010; Layoun et al., 2015), with the potential to collect additional cell types that may be present in the peritoneum. Collected cells are gently centrifuged, resuspended in complete culture medium containing fetal bovine serum (FBS), and plated in tissue culture plastics. TGEMs adhere to tissue culture plastics, while other cells such as red blood cells or adipocytes do not. Cells that have not adhered to the tissue culture plates can be eliminated by gentle washes with Dulbecco's phosphate-buffered saline (DPBS) and the remaining adhered cells can be used for analysis. In order to ensure that this preparation method provided a highly pure TGEM population for our studies and to determine if the age of the mouse from which the TGEMs were harvested impacted the degree of macrophage purity, we harvested peritoneal lavage exudates from five young (3 months old) and five old (20 months old) female C57Bl/6 mice 4 days post-thioglycollate induction. Cells collected from individual mice were treated as independent biological samples. We plated samples as described above and allowed the macrophage population to adhere overnight to the culture dishes. The next day, non-adherent cells were washed away using DPBS and we gently lifted the remaining adherent cells into freshly prepared ice-cold fluorescence-activated cell sorting (FACS) buffer. Each sample was stained for macrophage markers CD11b and F4/80. Flow cytometry analysis showed that approximately 95% of all cells stained double-positive for both markers, confirming these cells as macrophages. Moreover, we confirmed that the age of the mouse from which the cells were harvested does not impact the purity of the TGEM population obtained through this procedure (Figure 1—figure supplement 1). We utilized this method of thioglycollate induction and cell adherence to collect TGEMs for the experiments throughout this study.

Macrophage Mir146b expression levels decline during aging

Having established our method for obtaining TGEMs, we next performed RNA-seq analysis using small RNAs purified from TGEMs isolated from six time points (3 months, 6 months, 12 months, 18 months, 24 months, and 30 months) spanning the normal lifespan of C57Bl/6 mice to identify miRNAs whose expression changed with aging. Peritoneal exudates from 10 female thioglycollate-elicited mice were pooled to obtain one TGEM sample per time point. As miRNAs generally function to repress their targets, we were most interested in miRNAs whose expression decreased with organismal age, which would in turn lead to the accumulation of the miRNA’s downstream target genes that may contribute to age-associated inflammation and cellular dysfunction. Unsurprisingly, we found many miRNAs whose overall expression was altered with age (Figure 1—figure supplement 2A). We next validated the miRNAs whose expression seemed to decrease most gradually with increasing host age using quantitative polymerase chain reaction (qPCR). Each individual mouse used was treated as an independent biological replicate sample (n = 7–9 mice per age group). Each sample was run in duplicate for each probe set, and the average cycle threshold (Ct) value of the two technical replicates for each sample was used for computing the ΔΔCt and expression levels relative to our youngest (3 months old) time point. While our original RNA-seq dataset included mice as old as 30 months, the oldest mice available at the time of our validation were aged 20 months. Utilizing TGEMs from female mice at ages 3, 12, or 20 months, we observed no statistically significant differences (p<0.05) in expression levels of Mir15a, Mir29a, Mir423, Mir146a, or Mir18a with age. Mir362 (data not shown) was also evaluated but was undetected for approximately half of all samples tested, independent of age, and was therefore excluded from analysis. Two miRNAs, Mir146b and Mir22, displayed significant decreases from 3 to 20 months (Figure 1—figure supplement 2B–H). Previous studies in monocytes/macrophages have demonstrated that Mir22 is upregulated through PU.1 during hematopoetic differentiation and a loss of Mir22 is associated with acute myeloid leukemia (Shen et al., 2016; Jiang et al., 2016). Mir22 can act as either a tumor suppressor or an oncomiR, depending upon the context, and its expression has been shown to increase with aging in hearts (Huang and Wang, 2018; Wang et al., 2017; Jazbutyte et al., 2013). For Mir146b, prior works have demonstrated decreased expression in obesity and the progression and metastases of cancers including T-cell acute lymphoblastic leukemia and glioblastoma multiforme, and its expression has been noted to decrease with age in the lungs in human bronchial biopsies from healthy individuals (Hulsmans et al., 2012; Correia et al., 2016; Li et al., 2013; Ong et al., 2019). The effects of age-associated loss of neither Mir22 nor Mir146b have been previously demonstrated in macrophages. As the normalized expression of Mir146b in our RNA-seq dataset was greater than 3.3-fold at 3 months compared to that of Mir22 (Figure 1A and Figure 1—figure supplement 2I), indicating a potentially more biologically relevant target in this cell type, we chose to further investigate the role of Mir146b in TGEMs.

Figure 1 with 2 supplements see all
Expression of macrophage Mir146b declines with aging.

(A) Small RNA transcriptomic profiling of thioglycollate-elicited macrophages (TGEMs) across the mouse virtual lifespan identified Mir146b as a microRNA (miRNA) whose transcription levels progressively and unidirectionally decreased with host age (3- to 30-month-old C57Bl/6 females; TGEMs were pooled from n = 10 mice into a single sample per time point) (2207.22 reads per million [rpm] at 3 months vs 897.94 rpm at 30 months). (B) Representative quantitative polymerase chain reaction (qPCR) gene expression analysis of Mir146b from TGEMs from C57Bl/6 female mice (n = 9 vs n = 8 mice [biological replicates] from three independent experiments; *p = 0.0206) and (C) unstimulated bone marrow-derived macrophages (BMDMs) (n = 9 vs n = 9 mice [biological replicates] from three independent experiments; **p = 0.0040) from young (3 months) or old (30 months) C57Bl/6 female mice. (D) Relative qPCR expression levels of Mir146b in TGEMs compared to BMDMs (n = 3 vs n = 6 mice [biological replicates]; *p = 0.0238). (E) Relative Mir146b expression measured by Affymetrix QuantiGene 2.0 miRNA assay in TGEMs from 3-month- or 30-month-old female C57Bl/6 mice. Graph showing data from three independent experiments of n = 2 mice (biological replicates) per group (n = 6 vs n = 6; **p = 0.0022). Data for (B-E) are represented as mean ± SEM. Each point represents the mean value from two technical replicates per mouse. Mann-Whitney U-test was used for comparison between two groups.

Figure 1—source data 1

Flow cytometry density plots from TGEMs isolated from (n = 5) 3-month (bottom row)- and (n = 5) 20-month-old (top row) mice and stained with CD11b eFluor 450 and F4/80 Allophycocyanin (APC) along with numerical values for each mouse indicating the percentage of cells stained double-positive for both markers (CD11b+/F4/80+).

https://cdn.elifesciences.org/articles/66703/elife-66703-fig1-data1-v4.xlsx
Figure 1—source data 2

miRNA expression values in reads per million from small RNA-seq of female TGEMs at 3, 6, 12, 18, 24, and 30 months of age (related to Figure 1A and Figure 1—figure supplement 2A and I).

https://cdn.elifesciences.org/articles/66703/elife-66703-fig1-data2-v4.xls
Figure 1—source data 3

Relative miRNA expression values from qPCR of TGEMs from 3-month (n = 9 mice)-, 12-month (n = 8 mice)-, and 20-month (n = 9 mice)-old female mice.

Numerical values are displayed in graphs for (B) Mir146b, (C) Mir22, (D) Mir15a, (E) Mir29a, (F) Mir423, (G) Mir146a, and (H) Mir18a.

https://cdn.elifesciences.org/articles/66703/elife-66703-fig1-data3-v4.xlsx
Figure 1—source data 4

Relative Mir146b miRNA expression values used for the graph from qPCR of TGEMs from n = 9 young (3 months) or n = 8 old (30 months) female mice (related to Figure 1B).

https://cdn.elifesciences.org/articles/66703/elife-66703-fig1-data4-v4.xlsx
Figure 1—source data 5

Relative Mir146b miRNA expression values used for the graph from qPCR of BMDMs from n = 9 young (3 months) or n = 9 old (30 months) female mice (related to Figure 1C ).

https://cdn.elifesciences.org/articles/66703/elife-66703-fig1-data5-v4.xlsx
Figure 1—source data 6

Relative Mir146b miRNA expression values used for graphs from qPCR of young female TGEMs (n = 3 mice) vs BMDMs (n = 6 mice) (related to Figure 1D).

https://cdn.elifesciences.org/articles/66703/elife-66703-fig1-data6-v4.xlsx
Figure 1—source data 7

Relative Mir146b miRNA expression values used for graphs from Quantigene 2.0 assay of TGEMs from n = 6 young (3 months) or n = 6 old (30 months) female mice (related to Figure 1E).

https://cdn.elifesciences.org/articles/66703/elife-66703-fig1-data7-v4.xlsx

Mir146b expression is decreased with aging in TGEMs and BMDMs

Having noted that Mir146b expression decreased in TGEMs from 3 to 20 months, we next asked whether this decrease continued through 30 months of age (in accordance with our RNA-seq time points) and whether this trend also occurs in other macrophages, such as bone marrow-derived macrophages (BMDMs), which represent a more naive state compared to elicited TGEMs. We indeed observed a decrease of more than twofold in Mir146b gene expression in TGEMs isolated from 3-month-old vs 30-month-old female mice (Figure 1B), which was similar to the decrease noted for the same time points from our RNA-seq experiment. Levels of Mir146b also declined with age in BMDMs (Figure 1C); however, Mir146b relative expression in unstimulated BMDMs from individual 3-month-old female mice was only ~20% of that in TGEMs (Figure 1D). To further validate these findings, we used the hybridization probe-based Affymetrix QuantiGene 2.0 miRNA plate assay. We isolated total RNA from female TGEMs at 3 or 30 months of age and applied 250 ng of total RNA per well containing hybridization probe sets targeting either mature Mir146b or housekeeping gene U6 for overnight incubation. Two technical replicates per probe set were run for each biological sample/mouse. The following day, the signal was amplified and detected by using a chemiluminescent substrate according to the manufacturer’s instructions. Here we further confirmed a comparable decrease in Mir146b relative expression levels from 3 to 30 months in TGEMs (Figure 1E). Expression was below the limit of detection in naive BMDMs (data not shown) from both young and old female mice. Given the more robust expression of Mir146b in TGEMs, detectable without added stimulation, we continued our studies utilizing these cells exclusively.

Macrophage Mir146b regulates cytokine expression

Small RNAs such as miRNAs primarily regulate gene silencing within cells by binding to canonical target seed sequences in the 3’ untranslated regions (UTRs) of protein coding genes to initiate a process of post-transcriptional degradation (Bartel, 2009). Age causes poorly understood programmatic changes within the macrophage that lead to a shift in polarization that promotes dysfunctional immunity and disease (Wang et al., 2019). RNA-seq and subsequent qPCR validation demonstrated that Mir146b showed a consistent unidirectional alteration in expression pattern over the lifetime of a mouse. As such, we hypothesized that Mir146b target genes may either directly or indirectly regulate macrophage polarization and aging phenotype. High levels of Mir146b may promote a ‘young’ phenotype, and the progressive decline in its expression seen with increasing age likely leads to the alternative ‘old’ macrophage phenotype. To assess this hypothesis, we created macrophages (from young 3-month-old C57Bl/6 female mice) with transient Mir146b knockdown through fast-forward transfection with Mir146b-specific antagomir inhibitors at a final concentration of 25 nM. We measured the transfection efficiency (in separate wells) using a fluorescently tagged sham inhibitor of similar nucleotide length and noted a transfection efficiency of approximately 80%, with less than 2% cell death assessed by terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) (Figure 2A and data not shown) 24 hr post-transfection. This transfection protocol resulted in >50% reduction in macrophage Mir146b miRNA expression levels when measured by qPCR (Figure 2B). Knockdown of Mir146b for 72 hr in TGEMs resulted in trends of decreased gene expression of multiple cytokines and regulatory markers including Nos2, Mmp9, Il1b, Il6, Arg1, and Cd163 along with a trend toward increased Il10 (Figure 2C) when compared to the relative expression in sham-transfected control TGEMs. These results implicate Mir146b in regulating genes associated with macrophage polarization and inflammation, phenocopying aging macrophage phenotypes previously reported by our lab and others of decreasing M1-associated cytokine markers with simultaneously elevated Il10 (Kelly et al., 2007; Sene et al., 2013).

Figure 2 with 1 supplement see all
Loss of Mir146b in TGEMs alters inflammatory cytokine profile.

(A) Fast-forward transfection of thioglycollate-elicited macrophages (TGEMs) from 3-month-old C57Bl/6 female mice with small (~22 nucleotides) RNAs (magenta) for microRNA knockdown causes little apoptosis as determined by terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining (green/arrows) while (B) producing efficient knockdown of Mir146b vs scramble transfection control as determined by quantitative polymerase chain reaction (qPCR) expression analysis (n = 6 vs n = 6 from two separate experiments of n = 3 independent wells each; **p<0.0022). (C) Cytokine gene expression assessed by qPCR following Mir146b knockdown relative to sham control (blue dash line) in TGEMs. To obtain sufficient cell numbers for each experiment, TGEMs from n = 2–3 3-month-old female mice were pooled into a single sample. Each sample was run in two to three technical replicates, and the average Ct values were used for analysis. Each dot represents the normalized expression for one independently pooled sample relative to pooled littermate Control samples. (D) TGEMs from young female conditional knockout (cKO) mice have significantly reduced Mir146b gene expression levels (n = 6 vs n = 6 mice [biological replicates]; **p = 0.0049) (E) but no change in Mir146a compared to TGEMs from female littermate Controls (n = 6 vs n = 6 mice [biological replicates]; p = 0.5738 [not significant]). (F) TGEM cytokine gene expression from young female cKO mice vs littermate Controls (represented by the blue dashed line). Each dot represents the relative gene expression in cKO TGEMs pooled from two to three female mice per sample (as in C above) compared to that of pooled TGEMs from female littermate Controls for each independent experiment (n = 5 independent experiments). Graphical data are represented as mean ± SEM. Mann-Whitney U-test was used to compare between groups for (B), (D), and (E).

Figure 2—source data 1

Relative Mir146b miRNA expression values used for graphs from qPCR of sham-transfected (n = 6) or Mir146b inhibitor-transfected (n = 6) female TGEMs.

https://cdn.elifesciences.org/articles/66703/elife-66703-fig2-data1-v4.xlsx
Figure 2—source data 2

Relative mRNA expression values of Nos2, Il1b, Il6, Ccl2, Mmp9, Il10, Arg1, and Cd163 used for graphs from qPCR of sham-transfected (n = 3 independent biological samples) vs Mir146b inhibitor-transfected (n = 3 independent biological samples) pooled female TGEMs.

https://cdn.elifesciences.org/articles/66703/elife-66703-fig2-data2-v4.xlsx
Figure 2—source data 3

Relative Mir146b miRNA expression values used for graphs from qPCR of Control (n = 6 mice) vs cKO (n = 6 mice) TGEMs.

https://cdn.elifesciences.org/articles/66703/elife-66703-fig2-data3-v4.xlsx
Figure 2—source data 4

Relative Mir146a miRNA expression values used for graphs from qPCR of TGEMs from female Control (n = 6) vs cKO (n = 6) mice.

https://cdn.elifesciences.org/articles/66703/elife-66703-fig2-data4-v4.xlsx
Figure 2—source data 5

Relative mRNA expression values of Nos2, Il1b, Il6, Ccl2, Mmp9, Il10, Arg1, and Cd163 used for graphs from qPCR of pooled TGEMs from female Control (n = 8 independent biological samples) vs cKO (n = 7 independent biological samples) mice.

https://cdn.elifesciences.org/articles/66703/elife-66703-fig2-data5-v4.xlsx

Macrophages from mice with conditional Mir146b deletion demonstrated altered cell polarization

To assess the in vivo effect of the loss of macrophage Mir146b expression, we generated a novel Mir146b knockout mouse strain (Mir146bflox/flox) (Figure 2—figure supplement 1) and crossed it with the Lyz2Cre mouse line in order to generate mice with conditional Mir146b deletion in macrophages (hereafter referred to as cKO). Mir146bflox/flox littermates (hereafter referred to as Control) were used as controls. Mir146b loss was confirmed by qPCR using independent biological replicates from 3-month-old females (Figure 2D), and we determined that there was no compensation in expression of the Mir146 family member Mir146a (Figure 2E), whose mature sequence differs by only two nucleotides, both located outside of the seed region.

TGEMs harvested from young (6–12 weeks old) female cKO mice displayed altered polarization on gene expression analysis (Figure 2F) similar to that observed with Mir146b knockdown in TGEMs (Figure 2C). Compared to TGEMs isolated from littermate Controls, cKO TGEMs expressed lower levels of Nos2, Il1b, Il6, and Ccl2, traditionally associated with classical macrophage polarization, as well as Arg1 and Cd163. They also displayed a trend toward increased expression of the cytokine associated with alternative activation, Il10, all similar to the pattern observed following in vitro knockdown of Mir146b. However, Mmp9, which decreased following knockdown, showed an increasing trend in cKO TGEMs compared to littermate Controls. One possible explanation for this disparity may be that the life-long downregulation of Mir146b expression experienced by TGEMs in vivo results in different cellular stresses and additional regulation than a short-term (72 hr) partial knockdown in culture. Taken together, however, even without added activating stimulation (aside from potential effects of thioglycollate elicitation) such as IFNy + LPS or IL4, these results suggest that deficiencies in Mir146b lead to abnormal cytokine gene expression. Similar patterns have previously been reported with tumor-associated macrophages and macrophages in models of age-related macular degeneration (AMD), a blinding eye disease (Wang et al., 2019; Kelly et al., 2007; Sene and Apte, 2014; Sene et al., 2013; Mantovani et al., 2017).

TGEMs lacking Mir146b have phenotypically abnormal and functionally deficient mitochondria

In order to characterize Mir146b-deficient TGEMs on a subcellular level, we examined electron micrographs of cells isolated from cKO female mice and littermate Controls. Analysis revealed a decreased number of mitochondria in TGEMs lacking Mir146b compared to littermate Controls (Figure 3A–C). In addition, the mitochondria from cKO macrophages had increased intercrystal spaces (ICS) compared to Control TGEMs (Figure 3D–F), which may affect mitochondrial respiration and metabolic function.

Macrophages deficient in Mir146b have abnormal mitochondria with decreased functional capacity.

(A) Representative electron micrographs from Control and (B) conditional knockout (cKO) thioglycollate-elicited macrophages (TGEMs) from young female mice. Red asterisks indicate mitochondrial organelles. Scale bar = 2 μm. (C) Quantification of the average mitochondrial number per TGEM (n = 12 vs n = 10; *p = 0.0318). (D) Representative high-magnification electron micrograph images of mitochondria from young female Control and (E) cKO TGEMs. Scale bar = 500 nm. (F) Quantification of the average macrophage mitochondria intermembrane space width from young female cKO vs Control (n = 37 vs n = 100; ****p<0.0001) TGEMs. (G) MTT (3-[4,5-dimethylthiazol-2-yl]−2,5 diphenyl tetrazolium bromide) reduction by TGEMs from young (6–12 weeks old) female cKO vs Control (n = 7 vs n = 7 mice; ***p = 0.0006) TGEMs. (H) Oxygen consumption rates over time for young female cKO and Control TGEMs from representative Seahorse XF MitoStress assay (one-way analysis of variance [ANOVA], n = 3 vs n = 4 mice [biological replicates]; ****p<0.0001; Bonferroni’s multiple comparison test carbonyl cyanide 4-(trifluoromethoxy) phenylhydrazone (FCCP), **p<0.01). (I) Extracellular acidification rates over time for young female cKO and Control TGEMs (one-way ANOVA, ****p<0.0001; n = 3 vs n = 4 mice [biological replicates]; Bonferroni’s multiple comparison test FCCP, *p<0.05; antimycin A/rotenone (Ant/Rote), **p<0.01). (J) Oxygen consumption rates over time for young female C57Bl/6J TGEMs overexpressing Mir146b vs scramble control (one-way ANOVA, ****p<0.0001; n = 6 vs n = 8 biological replicates; Bonferroni’s multiple comparison test FCCP, ***p<0.001). (K) Extracellular acidification rates over time for young female C57Bl/6J TGEMs overexpressing Mir146b vs scramble control (one-way ANOVA, ****p<0.0001; n = 6 vs n = 8 biological replicates; Bonferroni’s multiple comparison test oligomycin, ****p<0.0001; FCCP, ****p<0.0001; Ant/Rote, ****p<0.0001). Data represent mean ± SEM. For (C), (F), and (G), Mann-Whitney U-test was used for comparison between groups.

Figure 3—source data 1

Representative x5000 transmission electron microscopy (TEM) images of Control (WT) TGEMs.

https://cdn.elifesciences.org/articles/66703/elife-66703-fig3-data1-v4.zip
Figure 3—source data 2

Representative x5000 TEM images of cKO TGEMs.

https://cdn.elifesciences.org/articles/66703/elife-66703-fig3-data2-v4.zip
Figure 3—source data 3

Numerical values used in graphs from the number of mitochondrial structures per cell in Control (Cntl) (n = 12) vs cKO (n = 9) female TGEMs.

https://cdn.elifesciences.org/articles/66703/elife-66703-fig3-data3-v4.xlsx
Figure 3—source data 4

Representative x25,000 TEM images of Control (WT) TGEMs.

https://cdn.elifesciences.org/articles/66703/elife-66703-fig3-data4-v4.zip
Figure 3—source data 5

Representative x25,000 TEM images of cKO TGEMs.

https://cdn.elifesciences.org/articles/66703/elife-66703-fig3-data5-v4.zip
Figure 3—source data 6

Numerical values used for graphs from measurements of intracrystal space width (nm) in TGEMs from female Control (WT) vs cKO TGEMs.

https://cdn.elifesciences.org/articles/66703/elife-66703-fig3-data6-v4.xlsx
Figure 3—source data 7

Numerical values of absorbance at 590 nM used for graphs from the MTT assay of female Control (Cntl) (n = 7 mice) vs cKO (n = 7 mice) TGEMs.

https://cdn.elifesciences.org/articles/66703/elife-66703-fig3-data7-v4.xlsx
Figure 3—source data 8

Numerical values used for graphs of the oxygen consumption rate (pmol/min) from Seahorse Mitostress Assay of TGEMs from female Control (n = 3 mice) vs cKO (n = 4 mice) TGEMs.

https://cdn.elifesciences.org/articles/66703/elife-66703-fig3-data8-v4.xlsx
Figure 3—source data 9

Numerical values used for graphs of the extracellular acidification rate (mpH/min) from Seahorse Mitostress Assay of TGEMs from female Control (n = 3 mice) vs cKO (n = 4 mice) TGEMs.

https://cdn.elifesciences.org/articles/66703/elife-66703-fig3-data9-v4.xlsx
Figure 3—source data 10

Numerical values used for graph of the oxygen consumption rate (pmol/min) from Seahorse Mitostress Assay of TGEMs from female Control transfected (n = 6) vs Mir146b overexpression (n = 8).

https://cdn.elifesciences.org/articles/66703/elife-66703-fig3-data10-v4.xlsx
Figure 3—source data 11

Numerical values used for graphs of the extracellular acidification rate (mpH/min) from Seahorse Mitostress Assay of TGEMs from Control transfected (n = 6) vs Mir146b overexpression (n = 8).

https://cdn.elifesciences.org/articles/66703/elife-66703-fig3-data11-v4.xlsx

Loss of TGEM Mir146b affects mitochondrial respiration

Given the abnormal number and structure of mitochondria in cKO TGEMs, we next examined the effects of loss of Mir146b on mitochondrial function. The tetrazolium salt 3-[4,5-dimethylthiazol-2-yl]−2,5 diphenyl tetrazolium bromide (MTT) is reduced into formazan crystals by oxioreductases, primarily (though not exclusively) in the mitochondria and provides an estimation of cell metabolism (Berridge and Tan, 1993). Compared to Control macrophages under standard tissue culture conditions, TGEMs from young female cKO mice reduce less MTT, indicating decreased metabolic activity (Figure 3G). To more specifically examine mitochondrial metabolism, we next utilized the Seahorse XF Mito Stress test to assess the oxygen consumption rate (OCR) as a measure of oxidative respiration. cKO TGEMs displayed a significant decline in oxidative phosphorylation (OXPHOS) maximal respiration, as assessed after treatment with the uncoupling agent carbonyl cyanide 4-(trifluoromethoxy) phenylhydrazone (FCCP) (Figure 3H), as well as decreased extracellular acidification rates (ECARs) (Figure 3I). Together, these results indicate that macrophages lacking Mir146b have decreased metabolic activity. Interestingly, when we overexpressed Mir146b mimic for 48 hr by transient transfection in TGEMs from young C57Bl/6J wildtype mice, the maximal mitochondrial respiration (OCR) rates and ECAR were increased compared to scramble-transfected controls (Figure 3J–K), providing further evidence that Mir146b levels influence gene expression and mitochondrial function in TGEMs.

Loss of macrophage Mir146b affects gene expression

We next sought to elucidate the molecular mechanism by which Mir146b contributes to aging, polarization, and mitochondrial dysfunction by performing RNA-seq of TGEMs from female cKO and littermate Control (Cntl) mice using four biological replicates from each group. Although genes upregulated in Mir146b-deficient macrophages are of interest as this pattern may indicate that Mir146b directly targets this/these gene(s) through canonical or non-canonical miRNA seed binding, we wanted to examine gene expression in a more global and unbiased manner so as to capture any significant changes in expression that may be related to mitochondrial dysfunction. We found several genes significantly downregulated in Mir146b cKO TGEMs, which have critical roles in both mitochondrial morphology and respiration (Sdhd, Crtc2, Pnpt1, Gdf15, Mrps28, Mterf3, Med30 Rnaseh1, Slc19a2, and Gtpbp10) (Gottlieb and Tomlinson, 2005; Linke et al., 2017; Shimada et al., 2018; Liu et al., 2019; Sylvester et al., 2004; Taylor and Turnbull, 2007; Krebs et al., 2011; Lima et al., 2016; Jungtrakoon et al., 2019; Lavdovskaia et al., 2018; Figure 4A). These findings suggest that macrophage Mir146b deficiency affects genes in the macrophage transcriptome that broadly regulate mitochondrial function and metabolism, leading to mitochondrial dysfunction and reduced metabolic capacity, similar to what is seen with aging (Xia et al., 2016; van Beek et al., 2019; Pence and Yarbro, 2018). Interestingly, only one gene, Lyz1, demonstrated a statistically significant increase in expression upon the loss of Mir146b in TGEMs. Lyz1 is a macrophage marker that has not previously been associated with mitochondrial dysfunction, and these results could not be replicated in mir146b overexpression experiments, where levels of Lyz1 were unaffected (data not shown).

Figure 4 with 2 supplements see all
Differential gene expression in cKO TGEMs by RNA-seq and single-cell RNA-seq.

(A) Transcriptome heatmap representation of differentially expressed genes from bulk RNA sequencing (RNA-seq) analysis of thioglycollate-elicited macrophages (TGEMs) from 3-month-old female conditional knockout (cKO) or littermate Controls (cKO n = 4 vs Control n = 4 mice [biological replicates]). (B) Transcription reads from single-cell RNA-seq of TGEMs from 3-month (young)- and 17-month (old)-old female cKO mice and age-matched littermate Control mice overlaid on a t-distributed Stochastic Neighbor Embedding (tSNE) plot separate into three distinct clusters. (C) Expression of macrophage markers Mertk, Csfr1, and Cd68 overlaid on tSNE plots. (D) Heatmap comparison of resident, recruited, and activation markers across the three clusters. (E) Heat map distribution of reads from single-cell RNA-seq of genes found to be differentially expressed by bulk RNA-seq. (F) Mean row z-score across the four samples (young Control, old Control, young cKO, and old cKO) for each hierarchically defined pattern (a–e) of genes differentially expressed by cells within the main cluster, Cluster 1. Individual genes within each pattern are shown in the Figure 4—figure supplement 2 heatmap and are listed in table form in Supplementary file 1.

Figure 4—source data 1

DESeq2 result file used for creating heatmaps from bulk RNA-seq of TGEMs from female Control vs cKO mice.

https://cdn.elifesciences.org/articles/66703/elife-66703-fig4-data1-v4.xlsx
Figure 4—source data 2

Normalized average expression of each expressed gene within the three clusters defined by scRNA-seq analysis of young Control, young cKO, old Control, and old cKO TGEMs from female mice.

https://cdn.elifesciences.org/articles/66703/elife-66703-fig4-data2-v4.txt
Figure 4—source data 3

Genes from clustering results used for creating heatmaps from scRNA-seq of young Control, young cKO, old Control, and old cKO TGEMs from female mice.

https://cdn.elifesciences.org/articles/66703/elife-66703-fig4-data3-v4.xlsx

Single-cell transcriptional profiling identifies three discrete macrophage populations and reveals cell origins

TGEMs are a heterogeneous population consisting of both long-lived resident tissue macrophages as well as recruited bone marrow-derived monocytic macrophages. As our bulk RNA-seq analysis uncovered only a small number of differentially expressed genes in Mir146b-deficient macrophages, without a single obvious candidate to directly explain mitochondria structural and functional disparities between cKO and Control cells, we hypothesized that the heterogeneity of our samples may be masking critical differences between the two genotypes. We also asked whether increasing age may amplify these transcriptional differences. To address these questions, we performed single-cell RNA sequencing (scRNA-seq) using TGEMs isolated from young (<4 months) and old (>17 months) cKO female mice and age-matched littermate Controls. Analysis revealed three transcriptionally distinct clusters (Figure 4B and Figure 4—figure supplement 1A). All clusters expressed macrophage markers including Mertk, Csf1r, and Cd68 (Figure 4C). Expression of markers including Apoe, Ms4a7, H2-DMa, C3, and Cd74 was enriched in Cluster 1, indicating that these cells represent mature recruited monocyte-derived macrophages specialized for antigen presentation. Meanwhile, canonical biomarkers of resident peritoneal macrophages Gata6, Vsig4, Timd4, and Marco were enriched in Cluster 2 cells. Resident macrophage markers were virtually absent from Cluster 3, but these cells showed elevated expression of genes corresponding to highly active recruited macrophages including Atp6v0d2, Htra1, Mfge8, and Chpt1 (Figure 4D).

We first sought to validate the bulk RNA-seq data and determine whether these changes were consistent across the three clusters or whether cell types were affected differently. A majority of the down-regulated genes identified by bulk RNA-seq displayed lower expression in clusters 1 and 3 of cells from cKO mice (Figure 4E). This loss of expression is consistent with a premature aging phenotype as many of the genes also show decreased expression in aged Control macrophages compared to young Control cells, although the resident peritoneal macrophages (Cluster 2) displayed equal or elevated expression of a subset of the genes. Lyz1, which was upregulated in cKO TGEMs in the bulk RNA-seq, was most highly expressed in resident peritoneal macrophages (Cluster 2). Its expression increased in cKO macrophages across all clusters compared to the Control counterparts; however, this increase was not recapitulated by natural aging (young Control vs old Control). As discussed above, we were unable to modulate Lyz1 expression using overexpression models. Taken together, these may suggest that Mir146b’s role in Lyz1 regulation is indirect. Alternatively, this discrepancy may be caused by amplifying or divergent effects due to the dramatic loss of Mir146b associated with our knockout model compared to the slow decline that occurs with natural aging, estimated to be approximately 25% (reduction) at this time point based upon our initial RNA-seq data (Figure 1A). These levels of Mir146b may either be sufficient to continue to regulate Lyz1, or the gradual rate of loss may allow for additional compensatory regulation by other factors.

Differential expression analysis reveals an increase in interferon-related transcripts in Mir146b knockout TGEMs

Since the majority of cells belonged to Cluster 1 (Figure 4—figure supplement 1B), we hypothesized that transcriptional differences amongst this population of cells would likely have contributed most to the observed mitochondrial dysfunction of Mir146b conditional knockout TGEMs. We analyzed differential gene expression of Cluster 1 cells between the four samples (young Control, young cKO, old Control, and old cKO) and identified five (a–e) hierarchically defined expression patterns amongst our four groups (Figure 4F, Figure 4—figure supplement 2, and Supplementary file 1). As with our bulk RNA-seq, we again primarily noted decreases in gene expression, with four out of five patterns showing loss of gene expression in old cKO macrophages compared to age-matched controls (patterns b–e). The genes within these patterns normally display significant age-dependent changes in Control TGEMs, but the scale of expression or expression change was significantly reduced upon loss of Mir146b. Genes within these patterns were enriched for cellular functions such as cholesterol transport and biosynthesis (pattern b), scavenging/phagocytosis and migration (pattern c), negative regulation of inflammation (pattern d), and calcium homeostasis and endocytosis (pattern e).

Pattern a, however, highlighted genes that normally show only small decreases with age but were significantly increased in aged cKO TGEMs. Interestingly, of the 28 genes differentially expressed in pattern a, six are integral to the interferon gamma (IFNy) signaling pathway, including interferon-stimulated gene 15 (Isg15), C-C motif chemokine ligand 5 (Ccl5), interferon-induced transmembrane 3 (Ifitm3), interferon regulatory factor 7 (Irf7), interleukin-18 binding protein (Il18bp), and secretory leukocyte protease inhibitor (Slpi). These findings are especially informative as IFNy treatment has been demonstrated to significantly reduce the respiratory capacity of macrophages in culture (Wang et al., 2018). These data further demonstrate that a loss of Mir146b in TGEMs may contribute to abnormal activation, polarization, mitochondrial dysfunction, and parainflammation as seen with macrophage aging.

Discussion

Diseases of aging are a systemic and global concern. Several cancers, atherosclerotic cardiovascular disease, neurodegenerative diseases such as Alzheimer’s, and blinding eye diseases such as AMD and glaucoma are examples of diseases associated with an exponential increase in prevalence with each passing decade of life (Nomellini et al., 2009; Klein et al., 2004; Ballard et al., 2011; Quigley, 2011). Macrophage-mediated inflammation has been implicated in promoting dysfunctional immunity in many of these disorders of aging; however, the molecular mechanisms by which aging leads to dysregulated macrophage function remain unknown.

Programmatic alteration of macrophage gene expression (coding transcriptome) with age precedes the downstream effects on macrophage proteome and function. miRNAs, which are major components of the non-coding transcriptome, alter expression by either transcript degradation or translational repression (Bartel, 2009). Here we completed a comprehensive non-biased profiling of the TGEM non-coding transcriptome by small RNA-seq of female mice 3–30 months of age (virtual lifespan) to probe for programmatic alterations in TGEM miRNA expression, which may contribute to the age-associated macrophage dysfunction that triggers the transition from physiologic aging to pathogenic inflammation and disease. We identified Mir146b and Mir22 as microRNAs whose expression progressively decrease with age in TGEMs from female mice. Mir146b expression was noted to be greater than 3.3-fold higher in macrophages from young (3 months old) mice compared to Mir22. While future studies examining the loss of Mir22 with aging in TGEMs are certainly warranted, here we chose to pursue the effects of Mir146b loss, as its initial high expression in TGEMs from young mice and gradual decline with age may represent a biologically relevant pattern consistent with the slow onset of age-related pathologies. One caveat of this study is that our original sequencing data, which identified Mir146b and Mir22 as miRNAs of interest in aging TGEMs, were aligned to the mm9 version of the mouse genome. While re-alignment and re-analysis of our data using the updated mm10 genome build may reveal other interesting patterns of miRNA expression or identify new miRNAs of interest in aging macrophages, this is technically challenging due to the legacy format of the raw data, and as such, we have not been able to integrate the data into the newer build. Regardless, our subsequent qPCR data confirm that Mir146b expression significantly decreases from 3 to 30 months in not only TGEMs but also BMDMs. We did note that Mir146b is expressed at much higher levels in TGEMs, owing, perhaps, to the naive state of cultured BMDMs compared to elicited macrophages. Loss of Mir146b in TGEMs, established by both the transient knockdown ex vivo and a novel conditional macrophage/monocyte knockout mouse, resulted in altered cytokine expression and polarization that parallels low-grade chronic inflammation associated with aging (van Beek et al., 2019).

We demonstrated that Mir146b deletion in TGEMs results in an abnormal mitochondrial structure and dysfunctional mitochondrial metabolism, characterized by a decreased OCR and ECAR. Interestingly, this phenotype was reversed with Mir146b overexpression. Further examination of the effects of Mir146b overexpression in TGEMs as well as other murine macrophages such as BMDMs is warranted in future studies.

Perhaps, not surprisingly, given our observed TEM and Seahorse findings, we identified altered gene expression of multiple mitochondria-related genes as well as genes critical to glucose metabolism in TGEMs deficient in Mir146b utilizing RNA-seq. Age-related reductions in oxidative phosphorylation have been noted not only in macrophages (Lin et al., 2018) but also in myriad cell types, including cardiomyocytes (Lesnefsky et al., 2016; Tepp et al., 2017), intestinal mucosal epithelial cells (Özsoy et al., 2020), and retinal pigment epithelium of AMD patients compared to age-matched healthy controls (Ferrington et al., 2017), and may serve as an important indicator of age-related disease and progression. Interestingly, the only gene noted to significantly increase with Mir146b conditional knockout in TGEMs by bulk RNA-seq was Lyz1, which encodes for the bacteriolytic enzyme lysozyme C-1. This gene has not been previously linked to mitochondrial function and was not significantly increased with age in scRNA-seq or altered in overexpression experiments. Our study identifies a novel role for Mir146b in mitochondrial metabolic function in macrophages within the innate immune system and suggests that a gradual loss of Mir146b expression with age contributes to TGEM dysfunction, possibly due to an altered IFN gene response. These changes in TGEM metabolism, activation, and polarization may contribute to the para-inflammation associated with aging and diseases of aging. Further research will be necessary to determine whether this age-associated dysfunction extends to macrophages from other tissues and whether it is therapeutically modifiable.

Materials and methods

Key resources table
Reagent type
(species) or
resource
DesignationSource or
reference
IdentifiersAdditional
information
Gene (Mus musculus)Mir146bEnsemblENSMUSG00000070127
Strain, strain background (M. musculus)Mir146bflox/floxThis paper (see'Materials and methods' and Figure 2—figure supplement 1)ENSMUSG00000070127Mouse line
Strain, strain background (M. musculus, female)C57Bl/6NIA Aged Rodent ColonyC57BL/6
RRID:SCR_007317
Strain, strain background (M. musculus, female)C57Bl/6The Jackson
Laboratory
000664
RRID:IMSR_JAX:000664
Strain, strain background (M. musculus, female)Lyz2CreThe Jackson
Laboratory
004781
RRID:IMSR_JAX:004781
Mouse line
Cell line (M. musculus)Primary thioglycollate-elicited macrophagesThis paper (see 'Materials and methods')C57Bl/6, 3–30 months;
Control or cKO, 3–17 months
Freshly isolated
Cell line (M. musculus)Primary Bone Marrow-Derived MacrophagesThis paper (see 'Materials and methods')C57Bl/6, 3–30 months; Control or cKO, 3 monthsFreshly isolated
Transfected construct (M. musculus)mirVana
Mir146b inhibitor
Life Technologies4464084
Transfected construct (M. musculus)mirVana negative controlLife Technologies4464076
Transfected construct (M. musculus)miRCURY LNA Mir146b mimicQiagenCat # 3391173
GeneGlobe ID: YM00472354-ABD
5’-UGAGAACUGAAUUCCAUAGGCU-3’
Transfected construct (M. musculus)miRCURY LNA negative control AQiagenCat# 300611-045’-/F6-FAM/AACACGTCTATACGC-3’
Sequence-based reagenthsa-Mir146b-5pQiagenCat# 339306
GeneGlobe ID: YP00204553
miRNA expression
Sequence-based reagenthsa-Mir146a-5pQiagenCat# 339306
GeneGlobe ID: YP00204688
miRNA expression
Sequence-based reagentU6 snRNAQiagenCat# 339306 GeneGlobe ID: YP00203907miRNA expression
Sequence-based reagenthsa-Mir-15a-5pQiagenCat# 339306
GeneGlobe ID: YP00204066
miRNA expression
Sequence-based reagenthsa-Mir-18a-5pQiagenCat# 339306
GeneGlobe ID: YP00204207
miRNA expression
Sequence-based reagenthsa-Mir-22-5pQiagenCat# 339306
GeneGlobe ID: YP00204255
miRNA expression
Sequence-based reagenthsa-Mir-29a-5pQiagenCat# 339306
GeneGlobe ID: YP00204430
miRNA expression
Sequence-based reagentmmu-Mir-362-5pQiagenCat# 339306
GeneGlobe ID: YP00205073
miRNA expression
Sequence-based reagenthsa-Mir-423-5pQiagenCat# 339306
GeneGlobe ID: YP00205624
miRNA expression
Sequence-based reagentQuantiGene
Mir146b-5p
AffymetrixCat# SM-10013-01miRNA expression
Sequence-based reagentQuantiGene
U6
AffymetrixCat# SR-19005-01miRNA expression
Sequence-based reagentUniversal Neo FThis paper (see Supplementary file 2)Genotyping forward primerTGC TCC TCG CGA GAA AGT ATC CAT CAT GGC
Sequence-based reagentUniversal Neo RThis paper (see Supplementary file 2)Genotyping reverse primerCGC CAA GCT CTT CAG CAA TAT CAC GGG TAG
Sequence-based reagentMir146b Neo FThis paper (see Supplementary file 2)Genotyping forward primerATA TCT GGC CCA CCA GGA ACA CAT
Sequence-based reagentMir146b Neo RThis paper (see Supplementary file 2)Genotyping reverse primerAGC CTC TGT GTG TGC TTG TGA CAT
Sequence-based reagentLoxP FThis paper (see Supplementary file 2)Genotyping forward primerTAA CGG CAT TAG CCA CCA CCT TCA
Sequence-based reagentLoxP RThis paper (see Supplementary file 2)Genotyping reverse primerTGG GTT ATG TAG GGA TCC TGG GTT
Sequence-based reagentFlp/o FWDThis paper (see Supplementary file 2)Genotyping forward primerATA GCA GCT TTG CTC CTT CG
Sequence-based reagentFlp/o REVThis paper (see Supplementary file 2)Genotyping reverse primerTGG CTC ATC ACC TTC CTC TT
Sequence-based reagentFlp/o Internal FWDThis paper (see Supplementary file 2)Genotyping forward primerCTA GGC CAC AGA ATT GAA AGA TCT
Sequence-based reagentFlp/o Internal RevThis paper (see Supplementary file 2)Genotyping reverse primerGTA GGT GGA AAT TCT AGC ATC ATC C
Sequence-based reagentLyz2Cre FThis paper (see Supplementary file 2)Genotyping forward primerGTA GGT GGA AAT TCT AGC ATC ATC C
Sequence-based reagentLyz2Cre RThis paper (see Supplementary file 2)Genotyping reverse primerTGG GTT ATG TAG GGA TCC TGG GTT
Sequence-based reagentActinb gene expression assayLife TechnologiesCat# 4352933E
Assay ID: Mm00607939_s1
mRNA expression
Sequence-based reagentGapdh gene expression assayLife TechnologiesCat# 4331182
Assay ID: Mm99999915_g1
mRNA expression
Sequence-based reagentNos2 gene expression assayLife TechnologiesCat# 4331182
Assay ID:
Mm00440502_m1
mRNA expression
Sequence-based reagentMmp9 gene expression assayLife TechnologiesCat# 4331182
Assay ID:
Mm00442991_m1
mRNA expression
Sequence-based reagentIl6 gene expression assayLife TechnologiesCat# 4331182
Assay ID:
Mm00446191_m1
mRNA expression
Sequence-based reagentIl1b gene expression assayLife TechnologiesCat# 4331182
Assay ID:
Mm01336189_m1
mRNA expression
Sequence-based reagentCcl2 gene expression assayLife TechnologiesCat# 4331182
Assay ID:
Mm00478593_m1
mRNA expression
Sequence-based reagentCd163 gene expression assayLife TechnologiesCat# 4331182
Assay ID:
Mm00474091_m1
mRNA expression
Sequence-based reagentArg1 gene expression assayLife TechnologiesCat# 4331182
Assay ID:
Mm00475988_m1
mRNA expression
Sequence-based reagentIl10 gene expression assayLife TechnologiesCat# 4331182
Assay ID:
Mm00439614_m1
mRNA expression
Chemical compound, drug3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromideMillipore-SigmaCat# M5655MTT
Chemical compound, drugThioglycollateMillipore-SigmaCat# T9032
AntibodyCD11b (Rat Monoclonal; (M1/70), eFluor 450)ThermoFisher48-0112-82
RRID:AB_1582236
Flow (1:100)
AntibodyF4/80 (Rat Monoclonal; (BM8), APC)ThermoFisher17-4801-82
RRID:AB_2784648
Flow (1:100)
Commercial assay or kitRNeasy Mini KitQiagenCat# 74104RNA extraction
Commercial assay or kitmirVANA RNA Isolation kitLife TechnologiesCat# AM1560RNA extraction
Commercial assay or kitQuantiGene 2.0 miRNA AssayAffymetrixCat# QS0008
Commercial assay or kitmiRCURY LNA RT kitQiagenCat# 339340
Commercial assay or kitHigh Capacity cDNA Reverse Transcription KitLife TechnologiesCat# 4368813
Commercial assay or kitApopTag Fluorescein In Situ TUNEL labeling KitMillipore-SigmaCat# S7110
Commercial assay or kitSeahorse XF Cell Mito Stress test kitAgilent TechnologiesCat# 103708-100
Software, algorithmATM Image Capture Engine V602 softwareAdvanced Microscopy Techniques
Software, algorithmCofactor EXP software packageCoFactor Genomics
Software, algorithmSTAR alignerGitHubRRID:SCR_004463
Software, algorithmQuant3pGitHubRRID:SCR_021236
Software, algorithmPhantasusBioconductorRRID:SCR_006442
Software, algorithmCellranger10x GenomicsRRID:SCR_017344
Software, algorithmMonocle3GitHubRRID:SCR_018685
Software, algorithmGraphpad PrismGraphpadRRID:SCR_002789
OtherTopCount NTX counterPerkin Elmer
OtherSpark multi-mode plate readerTecan
OtherSeahorse XF96 Extracellular Flux AnalyzerAgilent TechnologiesRRID:SCR_013575
OtherStepOne Plus Real-Time PCR SystemLife TechnologiesRRID:SCR_015805
OtherViia 7 Real-Time PCR SystemLife TechnologiesRRID:SCR_019582
OtherIllumina GAII sequencing platformCoFactor Genomics
OtherIllumina HiSeq2500 sequencing platformCentre for Applied Genomics; SickKidsRRID:SCR_001840
OtherNovaSeq S4 sequencing platformGTAC; Washington UniversityRRID:SCR_001030
OtherBD FACSCanto Flow Cytometry SystemBD BiosciencesRRID:SCR_018055

Animals

All animal use and experiments were approved by the Institutional Animal Care and Use Committee (IACUC) of Washington University in Saint Louis and performed according to the Washington University Animal Care and Use Guidelines. Data presented within this manuscript were obtained using female mice. C57Bl/6 mice, ranging in age from 3 to 30 months, were obtained from the National Institute on Aging (Bethesda, MA) Aged Rodent Colony. To create mice lacking Mir146b, a targeting vector that utilized a modified pBluescript backbone was constructed using recombineering methods (Lee et al., 2001). The first step was the retrieval of the entire length of the construct from the RP24-161H3 Bac vector. In the next step, the lone LoxP site, 326 bp downstream of Mir146b, was inserted. The last step was insertion of the LoxP and Frt flanked Neo cassette, 676 bp upstream ofMir146b. The construct contains a 5’ homology arm, a Pgk-driven Neo cassette flanked by Frt sites with a LoxP site upstream of the Neo cassette, a conditional arm with a lone LoxP site downstream of the conditional arm, and finally a 3’ homology arm. The 5’ arm starts at 3877 bp upstream of Mir146b and is 3201 bp in length. The conditional arm is 1118 bp and contains Mir146b. The 3’ arm starts 327 bp downstream of Mir146b and is 3117 bp in length. The linearized construct was transfected into SCC10 (129x1Sv/J) embryonic stem (ES) cells and clones were screened for G418 resistance and homologous recombination via long range PCR and Southern hybridization. Positive karyotypically normal ES clones were subsequently injected into mouse blastocysts. The resulting chimeric male mouse served as the colony founder and was bred to C57Bl/6J female mice (Jackson Laboratory, Bar Harbor; ME Stock No. 000664). Mice positive for the Mir146b insert were bred to B6.Cg-Tg(Pgk1-flpo)10Sykr/J hemizygous Flp deleter strain (Jackson Laboratory Stock No. 011065) to remove the Universal Neo cassette. Offspring positive for the Mir146b knock-in construct and negative for Frt/Universal Neo were further crossed to C57Bl/6J for subsequent generations to establish the line at >99% in the C57Bl/6J background. Genetic background analysis was performed by IDEXX BioAnalytics (Columbia, MO) using C57Bl/6J as the reference background. To create mice in which Mir146b was conditionally knocked out in macrophages, we crossed this line with Lyz2Cre mice (Jackson Laboratory stock No. 04781) to produce Lyz Cre Mir146bflox/flox (cKO) and MiR146bflox/flox littermate controls (Controls). The final breeding scheme of Lyz Cre Mir146bflox/flox X MiR146bflox/flox resulted in viable fertile litters with approximately half of the pups being Mir146b macrophage cKOs and half, Cre-negative (non-excised) Mir146b-construct-positive Controls. Genotyping primers and parameters can be found in Supplementary file 2.

Macrophage isolation

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Adult mice were injected interperitoneally with 1.5–2 ml of sterile 4% thioglycollate (Sigma; Saint Louis, MO) as previously described (Kelly et al., 2007; Nakamura et al., 2015; Sene et al., 2013; Khan and Apte, 2008; Santeford et al., 2016). At day 4 post-injection, mice were euthanized by CO2 asphyxiation and macrophages were collected by peritoneal lavage in 10 ml DPBS (Gibco [ThermoFisher Scientific]; Waltham, MA). Cells were pelleted at 1000 xg for 10 min, DPBS was decanted, and cells were resuspended and plated in Dulbecco's modified Eagle medium (DMEM) (Gibco) containing 10% FBS (Gibco), 100 U/ml penicillin/streptomycin antibiotic cocktail (Gibco), and 2 mM L-glutamine (Gibco). Following overnight cell adherence, plates were washed two to three times with DPBS to remove non-adherent cells and complete DMEM medium was replaced for approximately 24 hr, at which time they were directly assayed or harvested for further analysis. Cells were maintained in an incubator at 37°C with 5% CO2.

For BMDMs, mice were euthanized via CO2 asphyxiation, and the femurs and tibia were harvested. Each bone was flushed with 5 ml DMEM using a 25 g needle and syringe to collect the bone marrow. The cell suspension was passed through a 100 μM strainer to remove clumps. Cells were plated in a differentiation medium consisting of DMEM with 10% FBS, 1% L-glutamine, 1% pen/strep, 1% sodium pyruvate, and 20% conditioned medium from L929 cell culture. Cultures were washed in DPBS every 3 days and the differentiation medium replaced through d7, after which time they were switched to a medium without the addition of L929-conditioned media and prepared for assay.

Flow cytometry

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Cells were harvested by peritoneal lavage of thioglycollate-elicited female mice, aged 3 or 20 months, cultured overnight in complete DMEM to allow macrophage/monocyte cells to attach, washed with DPBS to remove non-adherent cells, and returned to culture to rest overnight as described above. To collect the adherent cells for analysis, plates were washed twice with DPBS to remove the residual culture medium and fresh ice-cold FACS buffer (1% bovine serum albumin and 0.05% sodium azide in DPBS) was added to the plate. Cells were gently removed using a cell lifter. The resulting cell suspension was pipetted up and down to achieve single cells and then passed through a 50-µM filter. Cells were stained with CD11b eFluor450 and F4/80 APC (eBiosciences) and 30,000 cells per sample were analyzed using a BD FACSCanto Flow Cytometry system.

Small RNA-seq of C57Bl/6 macrophages

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For small RNA-seq to examine miRNA expression, TGEMs were harvested, as described above, from C57Bl/6 female mice of ages 3, 6, 12, 18, 24, and 30 months (National Institute on Aging). Peritoneal exudates containing TGEMs were pooled from 10 mice per age group and plated and washed as described above. RNA (>10µg) was isolated from macrophage samples using the mirVana RNA isolation kit (ThermoFisher Scientific) as per manufacturer’s instructions. RNA was randomly fragmented and converted to complementary DNA (cDNA) for sequencing using the Illumina GAII (San Diego, CA) platform. The resulting sequencing reads were analyzed by Cofactor Genomics (St Louis, MO), in consultation with the Washington University Genome Center, using the Cofactor Genomics EXP software package. Sequences were first aligned against the mouse genome (July 2007 [NCBI37/mm9]). Overlapping reads were then clustered together to assemble expressed loci and provide respective read counts and coverage for each locus. All counts and expression levels were normalized down to the sample with the fewest reads in order to allow cross sample comparative expression between loci. A x6 coverage multiplier was used as a cutoff for including reads to compensate for stochastic deep sequencing. A pair-wise comparison was performed between samples and log2 ratios were computed for each expressed small RNA. Sequences aligning to the annotated regions of the mouse genome that have been previously identified as 572 individual microRNAs were used for further analysis. We next identified microRNAs whose expression either consistently increased or decreased (ie, unidirectional change) across time in the progressively aging macrophage, allowing for +/- 10% error in the expression ratios between any two consecutive time points, such that if the (n + 1) time point compared to (n) was 0.9 < (Exp(n+1))/Exp(n) < 1.1, then it was considered to be no change and not contributing to either direction. A heatmap of the top 100 expressed miRNAs was constructed using Phantasus build 1.9.2 (Zenkova et al., 2021).

miRNA expression profiling

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For qPCR analysis of miRNA expression, we isolated RNA from TGEMs using the miRvana RNA isolation kit and prepared cDNA using the miRCURY LNA Universal RT microRNA PCR, Polyadenylation and cDNA synthesis kit II (Qiagen) with 65 ng of starting RNA per reaction. We performed qPCR using ExiLENT SYBR Green master mix (Qiagen) and miRCURY LNA miPCR primer sets (Qiagen). To analyze the data, we used the ΔΔCt method, normalizing to U6 expression. Ct values greater than 36 were excluded from analysis. Relative miRNA expression was calculated using the average values obtained from the appropriate control group for each experiment (young [3 months] mice, sham-transfected controls, or Control mice). The following primer sets were utilized: hsa-Mir146b-5p (cat. # 339306, GeneGlobe ID YP00204553), hsa-Mir22-5p (cat. # 339306, GeneGlobe ID YP00204255), hsa-Mir15a-5p (cat. #339306, GeneGlobe ID YP00204066), hsa-Mir-29a-5p (cat. #339306, GeneGlobe ID YP00204430), hsa-Mir423-5p (cat. #339306, GeneGlobe IDYP00205624), hsa-Mir146a-5p (cat. #339306, GeneGlobe ID YP00204688), hsa-Mir18a-5p (cat. #339306, GeneGlobe ID YP00204207), hsa-Mir362-5p (cat. #339306, GeneGlobe ID YP0025073), and U6 snRNA (hsa, mmu) (cat. #339306, GeneGlobe ID YP00203907).

Mir146b expression analysis by QuantiGene Assay

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TGEMs were isolated and cultured as described above. RNA was extracted using mirVana RNA isolation kit. QuantiGene 2.0 miRNA Assay (Affymetrix; Santa Clara, CA) was performed according to the manufacturer’s instructions using 250 ng of RNA per reaction using Mir146b-5p probe set, positive control (#SM-10013–01), and U6 (#SR-19005–01). Briefly, diluted probe sets, samples, and controls were added to wells of the 96-well capture plate, sealed, and incubated overnight at 46°C. The following morning, plates were washed 3x with Wash Buffer, and Pre-Amp solution was applied to all wells for 60 min at 46°C. After washing, 2.0 Amplifier solution was applied to each well for 60 min at 46°C, followed by additional washes and application of the Label Probe for an additional 60 min at 46°C. After a final set of washes, luminescent 2.0 Substrate was added to each replicate well and luminescence was measured on a TopCount NTX counter.

Mir146b transient knockdown

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TGEMs were harvested from 3-month-old C57/Bl6 female mice. For inhibition of mmu-Mir146b, 5 x 105 cells in 600 ul media were plated in each well of a six-well plate and allowed to adhere. Lipofectamine RNAi MAX (ThermoFisher) was diluted 1:100 in a serum-free medium and combined 1:1 with a medium containing mirVana Mir146b inhibitor or negative control (Life Technologies, 4464084 or 4464076). This mixture was incubated at room temperature (RT) for approximately 15 min to allow for lipofectamine/oligo complexes to form, per manufacturer’s instructions. 400 ul of each solution was then added to cells for a final concentration of 2 ul lipofectamine and 25 nM inhibitor or negative control per well in 1 ml total volume. To assess transfection efficiency, additional cells were transfected using BLOCK-iT Alexa Fluor Red Fluorescent Oligo (ThermoFisher) using the same protocol. After 24–72 hr, cells transfected with BLOCK-iT were processed for TUNEL staining as described below, and cells transfected with Mir146b inhibitor or control sequence were harvested and processed for RNA/miRNA isolation using mirVANA microRNA isolation kit according to the manufacturer’s instructions (ThermoFisher).

TUNEL

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Chemicon ApopTag Fluorescein In Situ TUNEL labeling kit (Millipore/Sigma, Burlington, MA) was used according to the manufacturer’s instructions to detect apoptotic cells in cultured primary macrophage samples. Briefly, TGEMs were washed in DPBS and fixed in 1% paraformaldehyde for 10 min at RT and post-fixed in precooled ethanol:acetic acid (2:1) for 5 min at −20°C, with phosphate-buffered saline (PBS) washes before and after this step. Next, equilibration buffer was applied at RT followed by incubation with TdT enzyme at 37°C for 1 hr, and then a 10-min wash in Stop/Wash buffer. After washing in PBS, anti-digoxigenin conjugate was applied for 30 min, followed by PBS washes, counterstaining with 4 ′, 6-diamidino-2-phenylindole (DAPI), and fluorescent imaging.

mRNA expression

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For mRNA expression analysis, TGEMs were pooled from two to three female mice for each sample. We isolated RNA by using the RNeasy Plus Mini Kit (Qiagen) as per manufacturer’s instructions. We prepared cDNA using the High Capacity Reverse Transcription kit (Thermo Fisher Scientific) and performed qPCR using TaqMan Fast Advanced Master Mix (Thermo Fisher Scientific) with n = 2 technical replicates per sample. We used the ΔΔCt methods and normalized to the geometric mean of Actinb and Gapdh housekeeping genes. The following TaqMan Gene Expression probes were utilized: Actinb (Mm00607939_s1), Gapdh (Mm99999915_g1), Nos2 (Mm00440502_m1), Mmp9 (Mm00442991_m1), Il6 (Mm00446191_m1), Il1b (Mm01336189_m1), Ccl2 (Mm00478593_m1), Cd163 (Mm00474091_m1), Arg1 (Mm00475988_m1), and Il10 (Mm99999062). Each experiment was conducted three to five times using independent samples.

Transmission electron microscopy

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For ultrastructural analyses, TGEMs from female cKO or Control mice were fixed in 2% paraformaldehyde/2.5% glutaraldehyde (Polysciences Inc, Warrington, PA) in 100 mM sodium cacodylate buffer, pH 7.2, for 1 hr at RT. Samples were washed in sodium cacodylate buffer and post-fixed in 1% osmium tetroxide (Polysciences Inc) for 1 hr. Samples were then rinsed extensively in distilled water (dH20) prior to en bloc staining with 1% aqueous uranyl acetate (Ted Pella Inc, Redding, CA) for 1 hr. Following several rinses in dH20, samples were dehydrated in a graded series of ethanol and embedded in Eponate 12 resin (Ted Pella Inc). Sections of 95 nm were cut with a Leica Ultracut UCT ultramicrotome (Leica Microsystems Inc, Bannockburn, IL), stained with uranyl acetate and lead citrate, and viewed on a JEOL 1200 EX transmission electron microscope (JEOL USA Inc, Peabody, MA) equipped with an AMT 8.0 megapixel digital camera and AMT Image Capture Engine V602 software (Advanced Microscopy Techniques, Woburn, MA).

MTT reduction assay

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TGEMs from female mice were plated at 100,000 cells per well in 96-well plates in complete DMEM medium as described above. At the time of the experiment, cells were washed with DPBS and DMEM medium containing 0.5 mg/ml MTT (Millipore-Sigma) was applied to the cells for 3 hr at 37°C in a 5% CO2 incubator. Freshly prepared isopropanol containing 10% triton-X 100 and 0.1 N HCl was used to dissolve formazan crystals formed during the incubation. Absorbance at 570 nM was measured on a Tecan Spark multi-mode plate reader (Morrisville, NC).

Seahorse Mito Stress Assay

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For metabolic characterization of macrophages, we used the Seahorse XF Cell Mito Stress test on an XF96 Extracellular Flux Analyzer (Agilent Technologies, Santa Clara, CA) to measure the OCR as a surrogate marker for oxidative respiration. Macrophages were plated in Seahorse XF96 cell culture microplates (Seahorse Bioscience) at 100,000 cells per well. On the morning of the experiment, we washed the cells and replaced the medium with Seahorse assay medium (Agilent Technologies) supplemented with 10% FBS, 25 mM glucose (Millipore Sigma, St Louis, MO), and 1 mM sodium pyruvate (Thermo Fisher Scientific) and the pH adjusted to 7.4. After incubation in a non-CO2 incubator at 37°C for 1 hr, we measured the OCR at baseline and after sequential treatment with the following chemicals from the Mito Stress Test kit (Seahorse Bioscience): 3 μM oligomycin, 5 μM FCCP, and 1 μM rotenone/antimycin A (rot/AA). Each cycle consisted of 2 min of mixing and a 1 min pause, followed by a 5-min measurement period; we repeated each cycle three times. We normalized the background of all measurements by subtracting the average OCR of each sample after treatment with rot/AA. Values from n = 6 technical replicates were averaged for each biological replicate sample.

MicroRNA overexpression

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Transient overexpression of Mir146b in TGEMs was achieved by fast-forward transfection of cells harvested from C57Bl/6J (Jackson) female mice using fluorescein amidite-labeled (FAM-labeled) hsa-146b-5p miRCURY locked nucleic acid (LNA) miRNA Mimic (5’-UGAGAACUGAAUUCCAUAGGCU-3’; Qiagen; cat. #339173 YM00472354-ABD) or negative control A LNA (5’-/F6-FAM/AACACGTCTATACGC-3’; Qiagen; cat. #300611–04). Briefly, after harvest, macrophages were seeded at 1 x 106 cells/well in 96-well plates and allowed to adhere for 2 hr. Following DPBS washes to remove non-adherent cells, transfection complexes were prepared containing 50 nM of mimic (or negative control LNA) and 0.75% HiPerFect transfection reagent (Qiagen) and incubated for 15 min at RT before adding to the cells in DMEM supplemented with 10% FBS, 1% pen/strep, and 1% L-glutamine. FAM-labeled LNA allowed us to visualize the transfection efficiency by observing with a fluorescent microscope. Cells were assessed by Seahorse XF MitoStress Assay (as described above) 48 hr post-transfection.

RNA-seq of Mir146b-deficient macrophages and controls

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For bulk RNA-seq, TGEMs were harvested from 3-month-old female cKO and littermate Controls. Each mouse served as an independent biological replicate. Cells were plated and harvested as described above. RNA was isolated using RNeasy mini kit (Qiagen; Germantown, MD). mRNA was extracted with oligodT beads (Life Technologies), and cDNA and libraries were constructed as previously described (Bambouskova et al., 2018). Libraries were sequenced at the Centre for Applied Genomics (SickKids, Toronto) using a HiSeq2500 (Illumina) 50 x 25 bp pair-end sequencing. Fastq files for each sample were aligned to the mm10 mouse genome assembly using STAR aligner. Aligned reads were quantified using quant3p script to account for specifics of 3' sequencing with a protein coding subset of Gencode genome annotation. Differential expression analysis was determined by DESeq2 using the top 12,000 expressed genes. Heatmaps were constructed using Phantasus build 1.9.2 (Zenkova et al., 2021).

scRNA-seq

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For scRNA-seq, TGEMs were harvested from female cKO and littermate Control mice at 3 (15 weeks) or 17 months of age and plated as described above. Cells were profiled using the 10XGenomic platform using the 3’V3 chemistry and libraries sequenced on the Illumina NovaSeq S4 at the Genome Technology Access Center (GTAC) at Washington University.

Data were analyzed first using Cellranger 3.1.0 and mapped to the mouse mm10 genome with default parameters. Expression matrices were reanalyzed using Monocle3 (v0.2.1) (Cao et al., 2019). Initial clustering identified minor contamination (<1%) of cells not expressing macrophage markers (Emr1, Mertk, Csf1r, and Cd68) that were removed from further analyses. tSNE-dimension reduction was performed on the top principal components learned from the 317 genes with the highest variance across the cells that passed quality filters (>5000 mRNA counts), and the Louvain method was used to define clusters. The Monocle3 fit_models() function was used to assess differential gene expression with the following parameter: model_formula_str = ‘~library + 1’.

Data availability

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All sequencing data discussed herein have been deposited in NCBI’s Gene Expression Omnibus (Edgar et al., 2002) under record number GSE164476.

STAR aligner is available at https://github.com/alexdobin/STAR (Dobin, 2021) and quant3p script is available at https://github.com/ctlab/quant3p (copy archived at swh:1:rev:be9977925e9e842cc755f14ced72bbee5c5d6d77Sergushichev, 2021a).

Phantsus is available at https://github.com/ctlab/phantasus (copy archived at swh:1:rev:c6bb0e960554a23eb712690cbd7f8e3f7d79ca0dSergushichev, 2021b).

Cellranger is available at https://github.com/10XGenomics/cellranger (Marks, 2021).

Monocle3 is available at https://github.com/cole-trapnell-lab/monocle3 (brgew, 2021).

Statistical analysis

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Data are presented as mean ± SEM. Statistical evaluations were performed using GraphPad Prism Software version 9.1 (GraphPad, San Diego, CA). One-way mixed analysis of variance (ANOVA) with Bonferroni post-test or non-parametric Mann-Whitney U-test was used for comparison between groups. The accepted level of significance for all tests was p<0.05.

Data availability

Sequencing data have been deposited in GEO under accession code GSE164476.

The following data sets were generated
    1. Apte R
    2. Santeford A
    3. Sergushichev AA
    4. Ruzycki PA
    (2021) NCBI Gene Expression Omnibus
    ID GSE164476. Loss of macrophage miR-146b with aging contributes to inflammation and mitochondrial dysfunction.

References

Decision letter

  1. Bérénice A Benayoun
    Reviewing Editor; University of Southern California, United States
  2. Matt Kaeberlein
    Senior Editor; University of Washington, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

Age-related inflammation, also known as inflammaging, is a leading driver of multiple aging-related diseases. However, the molecular mechanisms underpinning why immune cells become dysfunctional during aging remain poorly understood. This study is a timely investigation demonstrating that aging leads to differential expression of micro RNAs in elicited peritoneal macrophages, which in turn promotes altered macrophage gene expression, polarization, and mitochondrial dysfunction. A key strength of this manuscript is the discovery of a role for miR-146b in macrophage biology, and the role of its decreased expression with age in driving aspects of macrophage functional decline with aging.

Decision letter after peer review:

Thank you for submitting your article "Loss of macrophage miR-146b with aging contributes to inflammation and mitochondrial dysfunction" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Matt Kaeberlein as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) The authors use a type of "elicited" macrophages, Thioglycollate-elicited macrophages (TGEMs), which do not represent a naive state, but an activated/recruited state. However, this information is only included in the material and methods, and not discussed in the rest of the manuscript. Since this could have a great impact on the results, the three reviewers agreed that it is essential that authors explicitly address the use of TGEMs, (even if only textually), including in the title, abstract and main text, to make sure that the narrower scope of findings is clear to readers without needing to read the material and methods. If possible (maybe for future studies), similar analyses of other macrophage types would help understand the general relevance of the finding on the impact of miR-146b on inflamm-aging.

2) Since the authors opted for an adherence-only method of purification for the TGEMs, it is crucial that some measurement of the purity of the macrophage population be provided to make sure that the purity of TGEMs by adherence is not affected by aging. An F4/80 and Cd11b flow cytometry staining on cells purified similarly from the same ages and sexes would be the ideal method for this.

3) The authors need to carefully edit the manuscript to include all relevant and necessary methodological details (e.g. sex of used mice in general and by panel, a systematic clarification of the use of technical vs. biological replicates, etc.). The authors also should refer to the individual reviewer comments for the points needed clarification in the revised manuscript on this point.

4) Generally, the authors need to improve and amend their statistical analyses. This includes (i) providing more information on the analysis leading to only miR-146b (since referees note that other miRs look significant in the analysis), (ii) removal of all t-tests since there is no power to test for data normality, removal of statistical tests when the authors only have n = 2, etc.

5) Finally, potentially contradictory findings between figures need to be reconciled or explicitly discussed by the authors. (e.g. Reviewer #2 point 4)

Reviewer #1:

In this manuscript, Santeford et al. study the regulation and impact of a microRNA, miR-146b, on macrophage aging phenotypes in mice. They first identify miR-146b as the only significantly and monotonously age-regulated miRNA in thioglycolate-elicited peritoneal macrophages (TGEMs) using a small RNA-seq approach. They then proceed to perform short-term manipulation of miR-146b expression in culture, as well as using a myeloid KO in vivo, and observe consistent remodeling of gene expression of cytokine genes in TGEMs. They also observe that miR-146b deficiency impacts TGEM metabolic function, including mitochondrial respiration. Transcriptome-wide analysis (both bulk and single-cell level) of the impact of miR-146b deficiency in TGEMs from the myeloid miR-146b KO model reveals decreased expression of metabolic genes and increases in interferon-signaling genes, similar to what is reported with aging.

This article identifies an interesting new regulator [miR-146b] of macrophage phenotypes with potential relationship with aging, thus providing an interesting mechanistic insight into remodeling of macrophage function with aging. Consequently, this study is of interest to the fields of macrophage biology, immunometabolism and aging biology. While the strength of this manuscript is the discovery of a role for miR-146b inn macrophage biology, and potential link with functional decline with aging, a number of points need to be addressed before the study is presented with its full context (i.e. choice of use of TGEMs) and some technical questions are answered.

A major caveat that will need to be discussed and addressed by the authors is the use of TGEMs vs. naïve/resident peritoneal macrophages with aging. Indeed, thioglycolate elicitation recruits new non-resident macrophages to the peritoneum, and will also drive them to a more activated state in response to the foreign signal. TGEMs contain a lot more "small" peritoneal macrophages (bone-marrow derived) than steady state peritoneal macrophages (usually composed of a majority of "large" peritoneal macrophages, which are resident and of embryonic origin). Although the study of TGEMs is interesting (and provides insights into populations recruited upon infection) they do not represent the steady state. This point should be very clearly stated in the text rather than just in the material and methods, at least the first time the macrophages are mentioned rather than just refer to these as "peritoneal macrophages" which is incorrect and somewhat misleading.

1) Peritoneal Macrophages used from these experiments are from thioglycolate-elicited peritoneal macrophages (majorly bone-marrow derived and recruited upon acute irritation), which are very different from the steady state population (embryonic derived and usually resting). Since thioglycolate is a way to mimic a chronic infection, some of the described biology (including the difference between TGEMs and BMDMs) could be due to a difference in response to the chronic infection instead of the difference between microRNA expression of aged animals. Thus:

a. This caveat needs to be explicitly addressed in the text the first time the authors mention the macrophages (line 80 "peritoneal macrophages"). This term should be systematically replaced with "TGEM" for accuracy throughout the manuscript.

b. The authors should discuss the caveat that effects may differ if unstimulated resident peritoneal macrophages had been evaluated instead.

c. Another point that should be discussed would be that the difference in age-regulated expression between TGEMs and BMDMs may be due to the effect of thioglycolate induction on the studied macrophages.

2) A number of studies are done with N = 2 samples (1E, 2C, 2F). This is problematic for several reasons: (i) statistics cannot be reliably applied to such small sample numbers and are thus meaningless and (ii) the use of the student t-test is for sure unwarranted, since a goodness of fit test is impossible to perform to confirm normality of data. If these pieces of data are retained as is, I recommend commenting on fold changes and nothing else. If the authors want to discuss statistics, additional samples need to be included and non-parametric tests should be used. In generally, the authors should revise analyses to use non-parametric tests instead of the Student t-test.

3) Aging is a very sex-dimorphic process, and thus a variable of interest in any study including aging as its topic. However, the sex of used animals is not given in the manuscript. Please update the manuscript to include this information. If only one sex was used, please discuss how results may be different in the other sex in the Discussion section. If both sexes were mixed, please make sure to color-code data points to differentiate females and males on the graphs.

4) Methodological details need to be included or revised for consistency reproducibility.

a. Please include a table with the sequences of all used qPCR and genotyping primers.

b. Some analyses are performed on the mm9 mouse genome build (e.g. small RNA-seq seq, line 449) and some on the mm10 genome build (e.g. RNA-seq of KO TGEMs line 591). Since this could lead to differences in results, please harmonize analyses so they are all performed on the same genomic build.

c. Please include all code/scripts used for the analysis in a supplementary document or deposit them to a Github repository as per the journal policy.

Reviewer #2:

Santeford et al. investigated age-dependent molecular changes in macrophages that could contribute to inflammaging. Using RNA-seq analysis on peritoneal macrophages from mice of various ages, they identified miR-146b as a microRNA that progressively and unidirectionally declined with age. Using miR-146b antagomirs (inhibitors) and conditional knockout mice, they show that the loss of miR-146b function alters the expression of several inflammatory cytokines. Microscopic analysis revealed abnormal mitochondrial morphology in thioglycolate-induced peritoneal macrophages that lack miR-146b, which was coupled with reduced maximum respiratory capacity and glycolytic rate. Single cell RNA-seq on peritoneal macrophages revealed distinct clusters and a subset with altered interferon γ signaling.

The study demonstrates an interesting role for miR-146b as a regulator of macrophage function. It provides a strong correlation between microRNAs and inflammaging, but a causal relation is yet to be established. The study surveys the expression of macrophage miR-146 from mice of various age groups, uses a unique mouse model capable of conditionally knocking out miR-46b, and scRNA-seq to dissect the complex population of peritoneal macrophages in a peritonitis model. Some limitations of the study lie on the lack of mechanistic connection between miR-146b and mitochondrial/metabolic alternations, the incomplete understanding whether aging macrophages lose the adaptive capacity to induce miR-146b upon stimulation or the ability to maintain baseline expression, and the unclear role of resident vs recruited macrophages in inflammaging.

1. The authors state that miR-146b is the only microRNA that progressively and unidirectionally declined with age (lines 25-27, lines 66-68). This is confusing as Supplementary Figure 1 shows other miRNAs that decline with age, such as miR15a. Please clarify and/or rephrase.

2. The thioglycolate-induced peritonitis model provides activated macrophages (both resident and recruited monocyte-derived macrophages). In contrast, bone marrow-derived macrophages that were differentiated in vitro are naive. This difference may underly the inconsistent in age-dependent miR-146 expression pattern in Figure 1. Thus, it is possible that what is lost during aging in macrophages is the adaptive capacity to induce miR-146b rather baseline expression. Would miR-146b levels decline with age also in bone marrow-derived macrophages if they were to be stimulated (i.e. loss of adaptive capacity to induce miR-14b)?

3. The manuscript should clearly discuss that data on thioglycolate-induced peritoneal macrophages reflect induced responses rather than naive conditions.

4. The cytokine panel in Figure 2C (miR-146b knockdown) does not include some cytokines in 2F (miR-146b knockout) – were they measured? Also, please comment on the Mmp9 expression, which is decreased in 2C but increased in 2F.

5. It would strengthen the paper if the levels of secreted cytokines (proteins) upon loss of miR-146b were measured.

6. In figure 3, the authors overexpress miR-146b and show increased mitochondrial respiration. Does this also alter the expression of cytokines measured in Figure 2?

7. The loss of miR-146b reduces OCR/ECAR and, conversely, its overexpression increases OCR/ECAR. Further, the authors show that the loss of miR-146b affects the expression of mitochondria-associated genes. On that line, does miR-146b overexpression affect similar genes (which would revert the metabolic phenotype)?

8. The miR-146b-dependent metabolic shift may result from alterations of multiple metabolic pathways that consequently affect OCR/ECAR, such as glucose metabolism. Were there metabolic genes that changed in the RNA-seq? If so, is/were there a coherent metabolic pathway(s) that is/are highlighted? If possible, quantifying metabolites that are highly relevant to macrophage function would provide further insight.

9. Are the cytokines measured in Figure 2 reflected in the scRNA-seq of Lyz2; miR-146bM-/M- mice?

10. As the authors state, peritoneal macrophages consist of a heterogeneous population of resident and recruited (monocyte-derived) macrophages. Further, monocyte-derived macrophages may not display age-dependent loss of miR-146b (Figure 1C). The authors may want to add some discussion on the potentially differential role of resident vs recruited macrophages in inflammaging. Further, have the authors tried to compare resident vs recruited macrophages in the scRNA-seq on peritoneal macrophages in addition to the 3 clusters (it is not clear whether the largest cluster 1 is a mix of both populations)?

Reviewer #3:

The manuscript by Santeford et al., investigates whether micro RNAs regulate macrophage function during the aging process. The authors approached this question by using an unbiased non-coding RNA transcriptomic profiling of mouse peritoneal macrophages spanning the whole lifespan of mice from 3-30 months. This analysis revealed an aged-dependent decrease in the expression of the micro RNA miR-146B. The authors further revealed that transient knock-down or knock-out of miR-146B expression in peritoneal macrophages leads to altered cytokine gene expression, indicative of skewed macrophage polarization seen in aging tissues. Additionally, miR-146B KO macrophages also had altered mitochondrial morphology and dysfunctional mitochondrial metabolism. Lastly, to further investigate peritoneal macrophage populations that are most affected by loss of miR-146B, the authors performed single-cell RNA sequencing of peritoneal macrophages from old and young WT and miR-146B KO mice. This analysis largely showed that gene expression in recruited monocyte derived (non-resident) macrophages is most affected by loss of miR-146B. The authors conclude that gradual loss of miR-146B may lead to macrophage dysfunction and inflammation phenotypes during aging.

Strengths:

– The authors use of unbiased non-coding RNA transcriptomic profiling of mouse peritoneal macrophages (spanning the whole lifespan of mice from 3-30 months) provides a thorough analysis of the expression profiles of multiple micro RNAs in an age-dependent manner. This approach allowed the authors to identify the age-dependent down-regulation in miR-146B expression. Furthermore, the authors data set also revealed age-dependent changes to other micro RNAs, which will be areas of future investigation and a great resource to the aging field.

– The authors utilized multiple genetics approaches to target miR-146B for transient knockdown and knockout in primary peritoneal macrophages and characterized the functional consequences. This approach led to the major findings in the paper that loss of miR-146B in macrophages leads to altered gene expression of inflammatory cytokines, metabolism genes, and mitochondrial dysfunction.

– In addition to in vitro based experiments, the authors developed a miR-146B KO mouse model and aged the mice to investigate how loss of miR-146B affected the aging phenotype of aging peritoneal macrophages. This analysis was largely done in an unbiased manner utilizing single-cell RNA sequencing.

Weakness:

– The authors claim that the unbiased non-coding RNA transcriptomic profiling revealed miR-146B as the only micro RNA with consistent progressive changes with age. However, careful analysis of Supplemental Figure 1A shows many other micro RNAs that appear to be both positively and negatively correlated with age, including miR-15a which has a nearly identical gene expression pattern to miR-146B.

– In Figure 1B, the authors attempt to validate the RNA-seq gene expression of miR-146B via qPCR in young and old peritoneal macrophages, to demonstrate that this micro RNA is down-regulated during aging. However, the authors do not attempt to look at other micro RNAs as controls to test their hypothesis that miR-146B is the only micro RNA whose expression is regulated during aging.

– Figure 1, the experimental details are not very clear as described in the main text or figure legends. For example, in all the experiments it is unclear whether the data represents individual mice or biological/technical replicates from an individual mouse, the sex of the mice used in the study is unclear, and many experiments have a small sample size, especially for being in vivo mouse experiments.

– The paper attempts to look at mechanisms of macrophage aging-related inflammation by solely focusing on thioglycolate induced peritoneal macrophages (a transient and non-resident monocyte derived subpopulation of macrophages) responding to acute inflammation. However, emerging evidence suggest that aging is characterized low-grade chronic inflammation. Thus, it's unclear whether miR-146B is relevant in aging-related inflammation since macrophages from naturally low-grade chronically inflamed aged tissues were not analyzed in this paper.

Furthermore, it is also unclear if miR-146B is differentially expressed in non-resident vs tissue resident macrophages as no attempt to measure this was done in the manuscript or in the single-cell data presented in Figure 4. This is especially relevant since the authors showed in Figure 1D that bone marrow derived macrophages do not express significant amounts of miR-146B, compared to peritoneal macrophages, suggesting that different population of macrophages may or may not express miR-146B, particularly those that drive inflammaging. Perhaps the authors could investigate macrophage phenotypes from other tissues (known to undergo inflammaging) such as fat tissue from young and old, WT and miR-146B KO mice.

– In Figure 2, the authors mention that loss of miR-146B affects macrophage polarization, skewing macrophages to a phenotype that resembles inflammaging. However, the authors only looked at a very narrow panel of cytokines. The data (both in vitro and in vivo) shows that loss of miR-146B leads to many pro-inflammatory cytokines being significantly down-regulated such as IL-1b and IL-6, while seeing an upregulation of the anti-inflammatory cytokine IL-10 suggesting miR-146B promotes an anti-inflammatory skewing (opposite of what the authors claim). Furthermore, this gene expression was performed under basal conditions which leads to less reliable gene expression. The authors did not attempt to measure how loss of miR-146B affects proper macrophage polarization via the treatment of macrophages with the type II cytokine IL-4 (M2) and LPS to skew to the classical M1 state.

– The data showing miR-146B regulates macrophage gene expression and mitochondrial function is descriptive and the authors do not provide any mechanistic insight in to how miR-146B promotes these changes.

-In Figure 4, the authors suggest the gene Lyz1 may be involved in the phenotype observed in miR-146B KO macrophages, but once again no attempt to demonstrate that miR-146B regulates mitochondrial function or gene expression via regulation of Lyz1 was performed. In fact, this conclusion is weakened by Figure 4E, showing that despite downregulation of miR-146B in old WT macrophages, Lyz1 expression does not increase as expected, making it an unlikely regulator of the altered gene expression seen in the WT old macrophages.

Overall, the authors of this study provided strong evidence that aging leads to the down-regulation of miR-146B expression in aging peritoneal monocyte derived macrophages. The authors have also provided strong evidence that miR-146B regulates cytokine expression and mitochondrial function in peritoneal macrophages. However, the paper suffers from being descriptive and lacking mechanistic insight in how miR-146B regulates macrophage cytokine expression and mitochondrial function. Lastly, for the reasons listed above the paper does not fully support the hypothesis that miR-146B may be a major driver of aging-related macrophage dysfunction and inflammaging.

1) Figure 1A, please clarify the units on the Y axis.

2) Typo pg 5, line 86, add space between number and months.

3) Figure 4D, clearly label what are resident vs non-resident markers.

4) Figure 4A and 4E, please list the genes in the same order and provide the same genes in each experiment.

5) Figure 4F, please label on graph what each Pattern represents, clearly state genes in each Pattern and if possible, show data for each gene in supplemental space.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Loss of Mir146b with aging contributes to inflammation and mitochondrial dysfunction in thioglycollate-elicited peritoneal macrophages" for further consideration by eLife. Your revised article has been evaluated by Matt Kaeberlein (Senior Editor) and a Reviewing Editor.

The reviewers have discussed your revised submission, and found that crucial issues had not been addressed, as outlined below:

1. The purity panel needs to be more than n = 1 per age, and should be included in the manuscript, not just in the rebuttal letter. All reviewers were disappointed that this major point was not satisfactorily addressed.

2. In general, the authors should address all previous concerns raised in the first round of reviews that were not addressed, including:

– a number of the methodological points we raised (for instance the mix and match approach on genome reference usage mm9/mm10) are not at all addressed, not even textually in the revised manuscript.

– regarding the uniqueness of the miR-146B pattern, reviewers are not convinced. For instance, the authors do not attempt to look at other micro RNAs as controls to test their hypothesis that miR-146B is the only micro RNA whose expression is regulated during aging.

– information about biological vs. technical replication is still lacking in the revised manuscript.

https://doi.org/10.7554/eLife.66703.sa1

Author response

Essential revisions:

1) The authors use a type of "elicited" macrophages, Thioglycollate-elicited macrophages (TGEMs), which do not represent a naive state, but an activated/recruited state. However, this information is only included in the material and methods, and not discussed in the rest of the manuscript. Since this could have a great impact on the results, the three reviewers agreed that it is essential that authors explicitly address the use of TGEMs, (even if only textually), including in the title, abstract and main text, to make sure that the narrower scope of findings is clear to readers without needing to read the material and methods. If possible (maybe for future studies), similar analyses of other macrophage types would help understand the general relevance of the finding on the impact of miR-146b on inflamm-aging.

Use of Thioglycollate-elicited macrophages (TGEMs) has been has been explicitly highlighted throughout the manuscript, and we have included this detail in the revised manuscript title.

2) Since the authors opted for an adherence-only method of purification for the TGEMs, it is crucial that some measurement of the purity of the macrophage population be provided to make sure that the purity of TGEMs by adherence is not affected by aging. An F4/80 and Cd11b flow cytometry staining on cells purified similarly from the same ages and sexes would be the ideal method for this.

In our previous studies, we have established that aging does not affect the purity of the TGEM population selected by adherence (Lin JB, Sene A, Santeford A, et al. Oxysterol Signatures Distinguish Age-Related Macular Degeneration from Physiologic Aging. EBioMedicine. 2018;32:9-20. doi:10.1016/j.ebiom.2018.05.035). Using a flow cytometry approach, as was also suggested by reviewers here, we harvested TGEMs from female C57Bl/6 mice at approximately 3 and 18 months of age and stained for macrophage markers F4/80 (clone BM8) and CD64 (clone x54-5/7.1). We acquired data on a BD LSR II flow cytometer and used FlowJo v10 software to visualize and analyze data. No difference in the macrophage population was noted with either marker, and the overall percentages of cells positive for both macrophage markers (and therefore identified as macrophages) was consistently 94-96% in both old and young. Author response image 1 contains representative dot plots from TGEM samples from a young and an old mouse, each gated for live single cells and double positive for F4/80 and CD64, as well as normalized univariate histogram displays for each marker.

Author response image 1

3) The authors need to carefully edit the manuscript to include all relevant and necessary methodological details (e.g. sex of used mice in general and by panel, a systematic clarification of the use of technical vs. biological replicates, etc.). The authors also should refer to the individual reviewer comments for the points needed clarification in the revised manuscript on this point.

Methodological details, including clarification of mouse sexes and ages and use of biological vs. technical replicates, have been extensively added throughout the manuscript for each experiment. This includes the Methods section as well as main text and figure legends.

4) Generally, the authors need to improve and amend their statistical analyses. This includes (i) providing more information on the analysis leading to only miR-146b (since referees note that other miRs look significant in the analysis), (ii) removal of all t-tests since there is no power to test for data normality, removal of statistical tests when the authors only have n = 2, etc.

We have added additional rationale regarding our identification of and focus on miR-146b, and acknowledged that other miRs were identified whose expression may change with mouse age and possibly warrant future investigation. In particular, reviewers noted that miR-15a levels represented in heatmap format in Supplemental Figure 1A look similar to those of miR-146b. Indeed, in this graphical representation pattern coloring does appear similar, particularly in small format. However, when we look directly at the numerical expression data, we can see that the decrease in expression from 3 months to 30 months in not unidirectional, as was seen with miR-146b and that may be expected as a result of the natural aging process. In fact, miR-15a expression actually increases by more than 10% between two separate consecutive time points (12 months to 18 months and 24 months to 30 months, respectively), thereby failing our criterion threshold.

Author response image 2

We have also presented experiments throughout the manuscript that include a greater number of replicates, when possible, and amended our statistical analyses of all experiments in accordance with the recommendation to remove all t-test. For instances when comparison between two groups is necessary, we have utilized the non-parametric Mann-Whitney U-test. In addition, for discussion of cytokine gene expression in Figures 2C and F, we removed statistical analysis and referred only to trends in the data, while also providing additional data points from independent experiments.

5) Finally, potentially contradictory findings between figures need to be reconciled or explicitly discussed by the authors. (e.g. Reviewer #2 point 4)

We have amended the text to address the contradictory findings noted by the reviewers. Namely, we have addressed the discrepancy between in vitro knockdown and in vivo knockout of miR-146b in regards to cytokine gene expression levels. One potential explain of the differential patterns that we observed may be caused by the dramatic long-term (life-long) absence of miR-146b in conditional knockout macrophages vs. the short-term, partial reduction achieved through in vitro transfection.

We have also expanded our discussion of Lyz1 data obtained through bulk RNA-seq and scRNA-seq. Lyz1 was found to be the only gene significantly increasing as a result of miR-146b deletion in TGEMs in our bulk RNAseq analysis. Deeper analysis using scRNA-seq also found this target to be increased across all clusters between miR-146b and littermate controls at both young (3 months old) and old (17 months old) time points. However, as noted by Reviewer #2, we did not observe an increase in Lyz1 when comparing old control TGEMs to young controls. As we have established that miR-146b expression is decreased with age in TGEMs, one may anticipate that expression of Lyz1 should thereby increase. An important consideration is that the natural aging process leads to a slow and steady decline of miR-146b, though not a full obliteration of expression, whereas TGEMs from our conditional knockout mouse model show a persistent, near complete miR-146b loss. The continued expression of miR-146b, though lesser with age, in control/wildtype TGEMs may either be enough to continue regulating Lyz1 and/or the slow decline in miR146b with aging may allow for additional, indirect compensatory regulation through other targets. These data illustrate in our opinion an important point about macrophage aging.

Reviewer #1:

1) Methodological details need to be included or revised for consistency reproducibility.

a. Please include a table with the sequences of all used qPCR and genotyping primers.

The list of qPCR probe sets and manufacturer ID/catalogue number has been presented in lieu of specific sequences. Each of the products used within this manuscript are commercially available. The manufacturers from which we obtained Taqman probes (Life Technologies) or LNA primers (Qiagen) do not disclose specific product sequences. However, utilizing the provided manufacturer assay IDs will allow other investigators to easily find these products should they choose to replicate these studies in their own hands.

All genotyping PCR sequences are presented in Supplemental Table 2 and the Key Resources table.

b. Some analyses are performed on the mm9 mouse genome build (e.g. small RNA-seq seq, line 449) and some on the mm10 genome build (e.g. RNA-seq of KO TGEMs line 591). Since this could lead to differences in results, please harmonize analyses so they are all performed on the same genomic build.

c. Please include all code/scripts used for the analysis in a supplementary document or deposit them to a Github repository as per the journal policy.

References to GitHub depository have been moved to the Data Availability section for greater visibility and included in the Key Resources table.

Reviewer #2:

1. The cytokine panel in Figure 2C (miR-146b knockdown) does not include some cytokines in 2F (miR-146b knockout) – were they measured? Also, please comment on the Mmp9 expression, which is decreased in 2C but increased in 2F.

In adding additional samples to our analyses, we have amended the list of cytokines to include targets measured for both knockdown and knockout, including some targets that may not be significantly changed. We have discussed any discrepancies within the text and noted possible rationale for differences noted in transgenic knockout vs. in vitro knockdown scenarios.

2. It would strengthen the paper if the levels of secreted cytokines (proteins) upon loss of miR-146b were measured.

We agree that protein levels often provide interesting insight to a cell’s biology. Our previous studies have shown that gene expression data provides a strong picture of the cell’s status. As our experiments did not utilize additional activators, such as INFγ + LPS or IL4, the greater sensitivity of qPCR over protein assays like ELISAs is capable of more accurately measuring differences here at baseline that may be otherwise less-reliably detectable due to the experimental limits of protein measuring.

3. In figure 3, the authors overexpress miR-146b and show increased mitochondrial respiration. Does this also alter the expression of cytokines measured in Figure 2?

The effects of miR-146b overexpression on TGEM cytokine or mitochondria-related gene expression have not yet been examined. These are interesting questions which certainly warrant follow-up studies, with both in vitro and possibly mouse models.

4. The miR-146b-dependent metabolic shift may result from alterations of multiple metabolic pathways that consequently affect OCR/ECAR, such as glucose metabolism. Were there metabolic genes that changed in the RNA-seq? If so, is/were there a coherent metabolic pathway(s) that is/are highlighted? If possible, quantifying metabolites that are highly relevant to macrophage function would provide further insight.

RNA-seq revealed statistically significant decreases in several genes involved in mitochondrial morphology and respiration, as noted in lines 342-3. However, no one pathway in particular seems to be targeted, though pathway analysis without a large number of genes can be limiting. Metabolomics analysis is great idea for future follow up studies, but is beyond the scope of the current project.

5. Are the cytokines measured in Figure 2 reflected in the scRNA-seq of Lyz2; miR-146bM-/M- mice?

Most of the cytokines measured in Figure 2 do appear in the scRNA-seq data set, as shown in Author response image 3. As many of the reads are scant, however, it is difficult to make broad assumptions regarding these targets in this context. For this reason, we did not include the data within the manuscript.

Author response image 3

6. As the authors state, peritoneal macrophages consist of a heterogeneous population of resident and recruited (monocyte-derived) macrophages. Further, monocyte-derived macrophages may not display age-dependent loss of miR-146b (Figure 1C). The authors may want to add some discussion on the potentially differential role of resident vs recruited macrophages in inflammaging. Further, have the authors tried to compare resident vs recruited macrophages in the scRNA-seq on peritoneal macrophages in addition to the 3 clusters (it is not clear whether the largest cluster 1 is a mix of both populations)?

BMDMs indeed exhibit age-related loss of miR-146b, though perhaps not as dramatically as noted in TGEMs. The levels of miR-146b even in BMDMs from young mice however is only ~30% that of TGEMs from age-matched mice. In the scRNA-seq, the majority of cells expressing markers or resident peritoneal macs map to cluster 2. However, the vast majority of cells map to cluster 1, as noted in Supplemental B, and as such would be anticipated to contribute most to the phenotypes we observed.

Reviewer #3:

1) Figure 1A, please clarify the units on the Y axis.

Y axis units of Figure 1A have been amended to “Normalized Expression (RPM)”. Units represent the normalized expression value of reads per million mapped to the mouse genome.

2) Typo pg 5, line 86, add space between number and months.

Spacing between number and months has been added to p5 line 86.

3) Figure 4D, clearly label what are resident vs non-resident markers.

Labels have been added to Figure 4D indicating resident vs recruited markers.

4) Figure 4A and 4E, please list the genes in the same order and provide the same genes in each experiment.

Gene listings for Figures 4A and 4E have been harmonized to list genes in the same order.

5) Figure 4F, please label on graph what each Pattern represents, clearly state genes in each Pattern and if possible show data for each gene in supplemental space.

For Figure 4F, the main text and figure legend has been expanded to more clearly explain that each row represents the mean z score for that hierarchically defined cluster of genes from the heatmap in Supplemental Figure 3. This was used to convey the various expression patterns across our four samples, and to interpret the GO categories and functions of the groups of genes with these patterns. All genes from Patterns a-e are noted in Supplemental Figure 3, along with their individual expression patterns across each sample, as well as listed Supplemental Table 1 for additional clarity.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The reviewers have discussed your revised submission, and found that crucial issues had not been addressed, as outlined below:

1. The purity panel needs to be more than n = 1 per age, and should be included in the manuscript, not just in the rebuttal letter. All reviewers were disappointed that this major point was not satisfactorily addressed.

2. In general, the authors should address all previous concerns raised in the first round of reviews that were not addressed, including:

– a number of the methodological points we raised (for instance the mix and match approach on genome reference usage mm9/mm10) are not at all addressed, not even textually in the revised manuscript.

– regarding the uniqueness of the miR-146B pattern, reviewers are not convinced. For instance, the authors do not attempt to look at other micro RNAs as controls to test their hypothesis that miR-146B is the only micro RNA whose expression is regulated during aging.

– information about biological vs. technical replication is still lacking in the revised manuscript.

1. We have included flow cytometry analysis of both young (3 month old, n=5 biological replicates/individual mice) and old (20+ month old, n=5 biological replicates/individual mice) TGEMs using the macrophage markers CD11b and F4/80 to validate the purity of the macrophage population utilizing the adherence selection method employed throughout this study. Approximately 95% of all cells, in both young and old samples, were double positive for both markers, indicating not only a high level of macrophage purity, but also demonstrating that there is no purity difference due to age. This data has been included as Figure 1—figure supplement 1 and Figure 1—figure supplement 1 source data. Dr. Lynn Hassman contributed to this effort and has been included as an author on the revised manuscript.

2. We have evaluated the miRNA expression patterns for a number of additional miRNAs in TGEMs. Here we further investigated microRNAs identified in our original dataset to have overall decreased expression with age, but which were not noted to decline in quite the linear fashion with each age point, we observed Mir146b. Based on values from our original small RNA-seq, we have evaluated the expression of Mir15a, Mir22, Mir423, Mir29a, Mir146a, and Mir18a along with Mir146b. We also attempted to analyze Mir362, but found its expression below the limit of detection for nearly half of our samples, regardless of age, and therefore did not include it in this manuscript. While our original experiment was able to utilize mice as old as 30 months of age, 20 month old mice is the oldest time point that we are able to presently acquire. We have noted this caveat within the manuscript. This new validation using mice using 7-9 individual mice at 3, 12, and 20 months of age again demonstrated decline in Mir146b from 3 to 20 months. In addition, Mir22 was observed to decline between these time points as well. Our RNA-seq data indicates that while there may be decreases in expression between 3 and 18-24 months, expression may actually increase again from 24-30 months. We have included the graph of normalized expression values for Mir22 obtained from our small RNA-seq data, but we cannot presently validate this potential increase in TGEMs from 30 month mice, and as such, do not comment on this pattern within the manuscript. We do however note that the level of reads for Mir146b is more than 3 fold higher than that of Mir22, keeping Mir146b an attractive target for our study, but also note the need for future studies of Mir22 in the aging macrophage.

3. We have explicitly highlighted and discussed that our original small RNA-seq data, which helped us to identify Mir146b as a microRNA of interest, was aligned to the mm9 version of the mouse genome. We understand that re-analysis of the data using a newer version may reveal different patterns of expression for some miRNAs, revealing additional miRNAs of interesting in aging. However, due to the changes in raw data file types and programs that have occurred in the time period since this data was originally procured, conversion has been technically challenging at this point, and as such we are currently unable to realign these files. Importantly though, our qPCR of miRNA expression and additional experiments validates our initial findings from this RNAseq data set—Mir146b progressively declines with aging in murine TGEMs.

4. We have reviewed details in both the manuscript body as well as figure legends to ensure that we have explicitly indicated the use of biological vs technical replicates of each experiment and added additional methodological details within both the results and Materials and methods sections as well as in the figure legends.

5. Use of Thioglycollate-elicited macrophages (TGEMs) has been has been explicitly highlighted throughout the manuscript, and we have included this detail in the revised manuscript title.

6. We have presented experiments throughout the manuscript that include a greater number of replicates, when possible, and amended our statistical analyses of all experiments in accordance with the recommendation to remove all t-test. For instances when comparison between two groups is necessary, we have utilized the non-parametric Mann-Whitney U-test. In addition, for discussion of cytokine gene expression in Figures 2C and F, we removed statistical analysis and referred only to trends in the data, while also providing additional data points from independent experiments.

7. We have amended the text to address the contradictory findings noted by the reviewers. Namely, we have addressed the discrepancy between in vitro knockdown and in vivo knockout of miR-146b in regards to cytokine gene expression levels. One potential explain of the differential patterns that we observed may be caused by the dramatic long-term (life-long) absence of miR-146b in conditional knockout macrophages vs. the short-term, partial reduction achieved through in vitro transfection.

We have also expanded our discussion of Lyz1 data obtained through bulk RNA-seq and scRNA-seq. Lyz1 was found to be the only gene significantly increasing as a result of miR-146b deletion in TGEMs in our bulk RNAseq analysis. Deeper analysis using scRNA-seq also found this target to be increased across all clusters between miR-146b and littermate controls at both young (3 months old) and old (17 months old) time points. However, as noted by Reviewer #2, we did not observe an increase in Lyz1 when comparing old control TGEMs to young controls. As we have established that miR-146b expression is decreased with age in TGEMs, one may anticipate that expression of Lyz1 should thereby increase. An important consideration is that the natural aging process leads to a slow and steady decline of miR-146b, though not a full obliteration of expression, whereas TGEMs from our conditional knockout mouse model show a persistent, near complete miR-146b loss. The continued expression of miR-146b, though lesser with age, in control/wildtype TGEMs may either be enough to continue regulating Lyz1 and/or the slow decline in miR146b with aging may allow for additional, indirect compensatory regulation through other targets. These data illustrate in our opinion an important point about macrophage aging.

https://doi.org/10.7554/eLife.66703.sa2

Article and author information

Author details

  1. Andrea Santeford

    Department of Ophthalmology and Visual Sciences, Washington University in St. Louis School of Medicine, St. Louis, United States
    Contribution
    Data curation, Formal analysis, Investigation, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7691-6213
  2. Aaron Y Lee

    Department of Ophthalmology and Visual Sciences, Washington University in St. Louis School of Medicine, St. Louis, United States
    Present address
    Department of Ophthalmology, University of Washington, Seattle, United States
    Contribution
    Conceptualization, Formal analysis
    Competing interests
    No competing interests declared
  3. Abdoulaye Sene

    Department of Ophthalmology and Visual Sciences, Washington University in St. Louis School of Medicine, St. Louis, United States
    Present address
    Kartos Therapeutics, Redwood, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation
    Competing interests
    No competing interests declared
  4. Lynn M Hassman

    Department of Ophthalmology and Visual Sciences, Washington University in St. Louis School of Medicine, St. Louis, United States
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  5. Alexey A Sergushichev

    Department of Pathology and Immunology, Washington University in St. Louis School of Medicine, St. Louis, United States
    Present address
    ITMO University, Computer Technologies Laboratory, Saint Petersburg, Russia
    Contribution
    Formal analysis
    Competing interests
    No competing interests declared
  6. Ekaterina Loginicheva

    Department of Pathology and Immunology, Washington University in St. Louis School of Medicine, St. Louis, United States
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared
  7. Maxim N Artyomov

    Department of Pathology and Immunology, Washington University in St. Louis School of Medicine, St. Louis, United States
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  8. Philip A Ruzycki

    Department of Ophthalmology and Visual Sciences, Washington University in St. Louis School of Medicine, St. Louis, United States
    Contribution
    Formal analysis, Visualization, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
  9. Rajendra S Apte

    1. Department of Ophthalmology and Visual Sciences, Washington University in St. Louis School of Medicine, St. Louis, United States
    2. Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, United States
    3. Department of Developmental Biology, Washington University in St. Louis School of Medicine, St. Louis, United States
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Validation, Methodology, Writing - original draft, Project administration, Writing - review and editing
    For correspondence
    apte@wustl.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2281-2336

Funding

National Institutes of Health (R01 EY019287-08)

  • Rajendra S Apte

Glenn Foundation for Medical Research

  • Rajendra S Apte

American Federation for Aging Research

  • Rajendra S Apte

Carl Marshall and Mildred Almen Reeves Foundation

  • Rajendra S Apte

Jeffery T. Fort Innovation Fund

  • Rajendra S Apte

Starr Foundation

  • Rajendra S Apte

National Institutes of Health (P30 EY02687)

  • Rajendra S Apte

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was supported by NIH grant R01 EY019287-08 (RSA), P30 EY02687 (Vision Core Grant), Glenn Foundation for Medical Research Award (RSA), American Federation for Aging Research Julie Martin Mid-Career Award (RSA), Carl Marshall Reeves and Mildred Almen Reeves Foundation Award (RSA), Jeffery T Fort Innovation Fund (RSA), the Starr Foundation (RSA), and an unrestricted grant from Research to Prevent Blindness to the John F Hardesty, MD Department of Ophthalmology and Visual Sciences at Washington University School of Medicine in Saint Louis. We would like to thank Jonathan B Lin, MD, PhD, for his assistance with statistical analysis.

Ethics

Animal experimentation: All animal use and experiments were approved by the Institutional Animal Care and Use Committee (IACUC) of Washington University in Saint Louis and performed according to the Washington University Animal Care and Use Guidelines (protocol numbers 2018-0160 and 20-0003).

Senior Editor

  1. Matt Kaeberlein, University of Washington, United States

Reviewing Editor

  1. Bérénice A Benayoun, University of Southern California, United States

Publication history

  1. Received: January 20, 2021
  2. Accepted: August 20, 2021
  3. Accepted Manuscript published: August 23, 2021 (version 1)
  4. Accepted Manuscript updated: August 25, 2021 (version 2)
  5. Accepted Manuscript updated: August 26, 2021 (version 3)
  6. Version of Record published: September 2, 2021 (version 4)

Copyright

© 2021, Santeford et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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