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. 2020 Nov 5;9:e55676. doi: 10.7554/eLife.55676

MicroRNAs of the miR-17~92 family maintain adipose tissue macrophage homeostasis by sustaining IL-10 expression

Xiang Zhang 1,2,3, Jianguo Liu 4, Li Wu 1,2,3, Xiaoyu Hu 1,2,3,✉
Editors: Florent Ginhoux5, Satyajit Rath6
PMCID: PMC7676864  PMID: 33150865

Abstract

Macrophages are critically involved in not only immune and inflammatory responses but also maintenance of metabolic fitness of organisms. Combined genetic deficiency of three clusters in the miR-17~92 family drastically shifted macrophage phenotypes toward the inflammatory spectrum characterized by heightened production of pro-inflammatory mediator TNF and diminished expression of anti-inflammatory cytokine IL-10. Consequently, macrophages residing in the adipose tissues from myeloid-specific miRNA triple knockout mice spontaneously developed inflammatory phenotypes and displayed alterations of overall physiological conditions as evidenced by obesity and compromised glucose tolerance. Mechanistically, miR-17~92 family miRNAs sustained IL-10 production by promoting transcription of the Fos gene, which is secondary to downregulation of Fos by transcription factor YY1, a direct target of miR-17~92 family miRNAs. Together, these results identified miR-17~92 family miRNAs as crucial regulators of the balance between pro- and anti-inflammatory cytokines and exemplified how macrophage-intrinsic regulatory circuit exerted impactful influence on general physiology.

Research organism: Mouse

Introduction

MicroRNAs are a class of short non-coding RNAs that regulate gene expression post-transcriptionally in metazoan organisms. miRNAs are processed from their primary transcripts (pri-miRNAs) that are cleaved by enzymes Drosha and DiGeorge syndrome critical region gene 8 (DGCR8) into ~70 nucleotide precursors (pre-miRNAs) that are further processed by Dicer into the ~22 nucleotide long mature miRNAs (Krol et al., 2010). For approximately 25–40% of miRNAs, their precursors are located in close proximity to other neighboring precursors on chromatin to form miRNA clusters (Altuvia et al., 2005; Kozomara and Griffiths-Jones, 2014), which typically yield mature miRNAs with distinct seed regions that may act coordinately to achieve common functions. miR-17~92 family miRNAs consist of three paralogous miRNA clusters (Mirc1 [called ‘miR-17~92’ here], Mirc2 [called ‘miR-106a~363’ here], and Mirc3 [called ‘miR-106b~25’ here]) that encode in total 13 distinct mature miRNAs, which can be further divided into four subfamilies (miR-17, miR-18, miR-19, and miR-25) according to their seed regions (Figure 1—figure supplement 1A and B; Mendell, 2008; Ventura et al., 2008). Genetic studies have shown that global deficiency of the miR-17~92 cluster results in embryonic lethality, whereas deletion of miR-106a~363 or miR-25~363 cluster yields no apparent phenotypes (Ventura et al., 2008). Functionally, miR-17~92 family miRNAs play an important role in developmental processes and are regarded as oncogenes during tumorigenesis (Mendell, 2008; Mogilyansky and Rigoutsos, 2013). In the immune system, miR-17~92 family miRNAs have been implicated in the maintenance of lymphocyte homeostasis, regulation of T follicular helper cell differentiation, and modulation of B cell development (Kang et al., 2013; Lai et al., 2016; Xiao et al., 2008). However, relatively little is known about the functions of miR-17~92 family miRNAs in myeloid cells including macrophages.

Macrophages play an essential role in the maintenance of tissue homeostasis and in innate immunity. In response to various environmental cues, tissue resident macrophages display a wide spectrum of phenotypes ranging from highly inflammatory ones typically observed upon infections to homeostatic ones during the processes of tissue repair. The spectrum of phenotypes could be partially attributed to the relative quantities of pro- versus anti-inflammatory mediators such as the antagonizing TNF-IL-10 pair (Mosser and Edwards, 2008; Sugimoto et al., 2019). The production of these pro- and anti-inflammatory cytokines is tightly controlled to achieve an intricate balance, and disruption of such balance has been causally linked with pathogenesis of an array of inflammatory and autoimmune diseases such as inflammatory bowel disease (IBD; Bain and Mowat, 2014). Moreover, the impact of these immune factors reaches far beyond the immune system evidenced by their effects on overall physiological and metabolic conditions. For example, during the development of obesity, pro-inflammatory cytokines such as TNF promote insulin resistance in adipose tissue (Chawla et al., 2011; Olefsky and Glass, 2010). In the obese adipose tissues, macrophages are the most abundant infiltrating immune cells and the major source of pro-inflammatory cytokines (Chawla et al., 2011; Odegaard and Chawla, 2011). Thus, the proper macrophage phenotypes in the adipose tissue microenvironment are critical for maintaining metabolic homeostasis.

In this study, we demonstrated that miR-17~92 family miRNAs critically controlled the balance between pro-inflammatory and anti-inflammatory phenotypes in adipose tissue macrophages (ATMs). Myeloid-specific deletion of three clusters of miR-17~92 family miRNAs twisted ATMs toward a spontaneously inflammatory phenotype characterized by diminished IL-10 and elevated TNF, leading to disrupted adipose homeostasis evidenced by obesity and compromised glucose tolerance. Mechanistically, miR-17~92 family miRNAs prevented inflammation by promoting IL-10 expression through a YY1-Fos-IL-10 regulatory circuit engaging both transcriptional and post-transcriptional mechanisms. Collectively, our results uncovered crucial roles of miR-17~92 family miRNAs in regulating macrophage inflammatory phenotypes in the context of adipose homeostasis.

Results

miR-17~92 family miRNAs protect mice from obesity

To investigate the function of miR-17~92 family miRNAs, we generated Mirc2−/− (called ‘miR-106a~363−/−’ here) Mirc3−/− (called ‘miR-106b~25−/−' here) Mirc1flox/flox (called ‘miR-17~92flox/flox’ here) Lyz2-Cre triple knockout (TKO) mice, which efficiently deleted all miRNAs in miR-17~92 family (Figure 1—figure supplement 2A–C). Interestingly, we found that TKO mice consistently exhibited increased body weights than age- and gender-matched Lyz2-Cre (WT) control mice at the age of 30 weeks (Figure 1A and Figure 1—figure supplement 3A–C). Then we isolated the visceral adipose tissue (VAT) and brown adipose tissue (BAT) from these mice and found that both the absolute weights of VAT and BAT and the percentages of VAT in TKO mice were significantly higher than those in WT mice (Figure 1B and C and Figure 1—figure supplement 3D). Furthermore, we scanned these mice with magnetic resonance imaging (MRI) and found that the total adipose tissue weight and its percentage of body weight were significantly increased in TKO mice (Figure 1D and E). Thus, these results indicated that miR-17~92 family miRNAs protected mice from obesity. To mimic this chronic obese phenotype, we fed TKO and WT mice with a high-fat diet (HFD). Consistent with the results obtained with animals under regular diet, body weights, VAT weights, and the VAT percentages of TKO mice were significantly higher than those of WT mice upon 20 weeks of feeding with high fat-diet (Figure 1F–H). Meanwhile, TKO and WT mice were scanned with MRI, and MRI quantitation indicated that both total adipose tissue weight and its percentage were significantly increased in TKO mice (Figure 1I and J). To further clarify the physiological role of miR-17~92 family miRNAs in ameliorating obesity, we generated mice deficient of miR-17~92 family miRNAs in the hematopoietic compartment by transferring bone marrow (BM) cells from WT and TKO mice into irradiated C57BL/6J or CD45.1 recipients (Figure 1—figure supplement 3E), and subsequently fed these chimeric mice with a regular chow diet or high-fat diet. TKO chimeric mice also exhibited increased body weights compared with WT chimeric mice (Figure 1K and Figure 1—figure supplement 3F). Moreover, the absolute weights and the percentages of VAT and total adipose tissue were significantly increased in TKO chimeric mice (Figure 1L–O). In summary, these results demonstrate that myeloid-intrinsic miR-17~92 family miRNAs protect mice from obesity.

Figure 1. miR-17~92 family miRNAs protect mice from obesity.

(A) A representative photograph (left panel) and body weight (right panel) of the Lyz2-Cre (WT) and miR-106a~363−/− miR-106b~25−/− miR-17~92flox/flox Lyz2-Cre (TKO) mice at the age of 30 weeks, n = 14–16 per group. (B and C) Visceral adipose tissue (VAT) weight (B) and VAT percentage of body weight (C) of WT and TKO mice at the age of 30 weeks. (D and E) Total adipose tissue weight (D) and its percentage of body weight (E) of WT and TKO mice were measured by scanning the whole mice with magnetic resonance imaging (MRI) machine at the age of 30 weeks. (F–J) WT and TKO mice were fed with a high-fat diet (HFD) and were sacrificed on 13–20 weeks post HFD. Body weight of each mouse was measured weekly, n = 8 per group (F); VAT weight (G), VAT percentage of body weight (H), total adipose tissue weight (I), and its percentage of body weight (J) were measured. (K–O) C57BL/6J mice were irradiated and transferred with WT (WT → WT) or TKO (TKO → WT) bone marrows (BMs). Then these BM chimeras were fed with an HFD and were sacrificed on 16–28 weeks post HFD. Body weight of each mouse was measured weekly, n = 7–8 per group (K); VAT weight (L), VAT percentage of body weight (M), total adipose tissue weight (N), and its percentage of body weight (O) were measured. *p<0.05, **p<0.01, ***p<0.001, and ****p<0.0001 (unpaired Student’s t-test). Data are pooled from four (A–C), two (D, E, F, K, L, and M), or five (G and H) independent experiments (mean ± s.e.m.), or are representative of one independent experiment (I, J, N, and O).

Figure 1.

Figure 1—figure supplement 1. Schematic representation of the miR-17~92 family miRNAs.

Figure 1—figure supplement 1.

(A and B) Schematic representation of three miRNA clusters of miR-17~92 family miRNAs (A) and their target sequences (B). The mature miRNAs in different families are shown in different colors. Yellow: members of the miR-17 family; Red: members of the miR-18 family; Blue: members of the miR-19 family; Green: members of the miR-25 family. The ‘seed region’ of each mature miRNA is shown in bold format.
Figure 1—figure supplement 2. The miR-17~92 family miRNAs are efficiently deleted in TKO BMDMs.

Figure 1—figure supplement 2.

(A–C) qPCR analysis of various mature miRNAs (horizontal axes) in the miR-17~92 cluster (A), miR-106a~363 cluster (B), and miR-106b~25 cluster (C) in Lyz2-Cre (WT) and miR-106a~363−/− miR-106b~25−/− miR-17~92flox/flox Lyz2-Cre (TKO) BMDMs; results are presented as relative expression normalized to the control small RNA U6. Data are representative of two independent experiments (mean + s.d.).
Figure 1—figure supplement 3. miR-17~92 family miRNAs play an important role in protection of mice from obesity.

Figure 1—figure supplement 3.

(A–B) Body weights of the Lyz2-Cre (WT) and TKO male (A) or female (B) mice at the age of 30 weeks (n = 7–9 per group). (C) Body weights of WT and TKO mice fed with regular chow diet, n = 4 per group. (D) Brown adipose tissue (BAT) weights of WT and TKO mice at the age of 30 weeks. (E) CD45.1 mice were irradiated and transferred with 106 donor bone marrow (BM) cells from WT (WT → WT) or TKO (TKO → WT) mice. Six weeks post BM transfer, CD45.1 and CD45.2 expression on adipose tissue macrophages (ATMs) was analyzed by flow cytometry. One representative experiment was shown in the left panel and cumulative data were shown in the right panel (n = 4 per group). (F) Body weights of chimeric mice transferred with WT (WT → WT) or TKO (TKO → WT) BMs fed with regular chow diet (n = 8 per group). Each symbol represents an individual mouse. *p<0.05, **p<0.01 and ****p<0.0001 (unpaired Student’s t-test). Data are representative or are pooled from two (A, B, D, and E) or one (C and F) independent experiments (mean ± sem).
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miR-17~92 family miRNAs maintain adipose tissue macrophage homeostasis

We next thought to investigate the mechanisms by which miR-17~92 family miRNAs protected mice from obesity. As obesity is often associated with insulin resistance, we first examined the glucose homeostasis in TKO and WT mice with glucose tolerance test (GTT) and found that TKO mice showed higher insulin resistance than control animals (Figure 2A). It is well established that pro-inflammatory cytokine TNF and anti-inflammatory cytokine IL-10 are critical in regulating the insulin sensitivity and obesity (Chawla et al., 2011; Olefsky and Glass, 2010), so we first detected TNF and IL-10 levels in the serum of WT and TKO mice and found increased TNF and decreased IL-10 proteins in TKO mice relative to WT animals (Figure 2—figure supplement 1A and B). Then we examined the expression of Tnf and Il10 mRNA in WT and TKO VAT by quantitative real-time PCR (qPCR). We found that the expression of Tnf was significantly higher, whereas the expression of Il10 was significantly lower in TKO VAT than those in WT VAT (Figure 2B and Figure 2—figure supplement 1C and D) As macrophages are reported to be the major immune cells producing these cytokines in the VAT (Chawla et al., 2011; Odegaard and Chawla, 2011), we first analyzed the ATM population in VAT of WT and TKO mice. miR-17~92 family miRNAs were efficiently deleted in TKO ATMs (Figure 2—figure supplement 2A–E) and their deficiency did not alter the population of ATMs in mice (Figure 2C and D and Figure 2—figure supplement 3A–E). Furthermore, genome-wide RNA profiling analyses were performed with fluorescence-activated cell sorting (FACS) sorted ATMs from WT and TKO mice fed with regular chow diet or high-fat diet. Gene ontology (GO) analysis indicated that genes differentially expressed in both TKO versus WT mice and high-fat diet versus chow diet fed WT mice were enriched in the inflammatory response category (Figure 2E and Figure 2—figure supplement 4A). Meanwhile, the inflammatory score indexing cellular inflammatory phenotypes calculated based on the RNA sequencing data indicated that ATMs from TKO mice were more inflammatory than the WT counterparts under both regular chow diet and high-fat diet conditions (Figure 2—figure supplement 4B). Moreover, both RNA sequencing results and qPCR results showed that TKO ATMs expressed increased Tnf and decreased Il10 (Figure 2F–H and Figure 2—figure supplement 4C and D). Thus, these results demonstrated that miR-17~92 family miRNA deficiency shifts ATM toward an inflammatory phenotype characterized by TNF overproduction in the absence of exogenous infectious or inflammatory signals, suggesting that miR-17~92 family miRNAs maintain ATM homeostasis to prevent obesity and to facilitate glucose tolerance.

Figure 2. miR-17~92 family miRNAs maintain adipose tissue macrophage (ATM) homeostasis.

(A) Glucose tolerance test in WT and TKO mice (left panel); area under curve (AUC) of left panel (right panel), n = 6 per group. (B) qPCR analysis of Tnf and Il10 mRNA in visceral adipose tissue of WT and TKO mice. (C and D) Flow cytometry of ATM population in WT and TKO mice. (C) A representative figure showing the ATM population of CD45+ cells; (D) cumulative quantification of ATM population as in (C). (E and F) ATMs were sorted from WT and TKO mice fed with regular chow diet, RNA samples of ATMs were extracted and pooled from two mice, and genome-wide RNA profiling analyses were performed. (E) Gene ontology (GO) analysis of WT and TKO ATM RNA-seq datasets showing the enriched GO terms in TKO ATMs; (F) RNA-seq analysis showing RNA expression in TKO ATMs versus those in WT cells. RNAs upregulated in TKO ATMs were colored red, whereas RNAs downregulated were colored blue, gene Tnf was pointed out and colored orange, and gene Il10 was pointed out and colored green. Top 10 upregulated genes (Ighg2b, Gm11843, Dcdc2a, Mmp7, Slc38a5, Tenm2, Cdh16, Ptn, Ighv1-42, and Nanp) were colored yellow and top 10 downregulated genes (Csf2ra, Zfp125, Cxcl5, Ighv14-3, Igkv4-59, Igkv6-20, Igkv16-104, Fth-ps2, Igkv4-91, and B230303A05Rik) were colored bright blue. (G and H) qPCR analysis of Tnf (G) and Il10 (H) mRNA in ATMs of WT and TKO mice. Results were shown as relative expression normalized to those expressions in one WT mice of each experiment. NS, not significant (p>0.05); *p<0.05, ***p<0.001, and ****p<0.0001 (unpaired Student’s t-test). Data are representative of three independent experiments (C) or are pooled from two (A), three (B and D), or five (G and H) independent experiments (mean ± sem).

Figure 2.

Figure 2—figure supplement 1. miR-17~92 family miRNAs inhibit TNF and promote IL-10 expression in adipose tissue.

Figure 2—figure supplement 1.

(A and B) Protein levels of TNF (A) and IL-10 (B) in serum of WT and TKO mice measured by ELISA. (C and D) qPCR analysis of Tnf (C) and Il10 (D) mRNA with additional primer pairs in WT and TKO adipose tissues. *p<0.05, **p<0.01 (unpaired Student’s t-test). Data are pooled from three (A–D) independent experiments (mean ± s.e.m.).
Figure 2—figure supplement 2. The miR-17~92 family miRNAs are efficiently deleted in TKO adipose tissue macrophages (ATMs).

Figure 2—figure supplement 2.

(A and B) Flow cytometry analysis of RFP expression in ATMs (A) and peritoneal macrophages (B, positive control) from Lyz2-Cre and Lyz2-RFP mice. (C–E) qPCR analysis of various mature miRNAs (horizontal axes) in the miR-17~92 cluster (C), miR-106a~363 cluster (D), and miR-106b~25 cluster (E) in Lyz2-Cre (WT) andTKO ATMs; results are presented as relative expression normalized to the control small RNA U6. Data are representative of two independent experiments (mean + s.d.).
Figure 2—figure supplement 3. The adipose tissue macrophage (ATM) populations are not changed in TKO mice.

Figure 2—figure supplement 3.

(A–E) Flow cytometry analysis of CD11c (A), CD64 (B), CX3CR1 (C), MerTK (D), and Ly6C (E) expression in ATMs from Lyz2-Cre (WT) and TKO mice. Data are representative of two independent experiments.
Figure 2—figure supplement 4. Adipose tissue macrophages (ATMs) in TKO mice are more inflammatory.

Figure 2—figure supplement 4.

(A and B) WT and TKO mice were fed with regular chow diet or high-fat diet (HFD) for 8 weeks; ATMs were sorted for a genome-wide RNA profiling analysis. (A) Gene ontology analysis of RNA-seq datasets of ATMs from WT mice fed with chow diet or HFD. (B) Inflammatory score of WT and TKO mice fed with chow diet or HFD. Inflammatory score = sum of log (FPKM+1) of representative pro-inflammatory genes (Tnf, Il6, Cxcl1, Ccl2, and Ccl8) − sum of log (FPKM+1) of representative anti-inflammatory genes (Il10, Il4, Il5, and Il13). (C and D) qPCR analysis of Tnf (C) and Il10 (D) mRNA with an additional set of primers in WT and TKO ATMs. *p<0.05 (unpaired Student’s t-test). Data are representative of one (A and B) or are pooled from two (C and D) independent experiments (mean ± s.e.m.).
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miR-17~92 family miRNAs balance TNF and IL-10 production in macrophages

To circumvent the technical limitation imposed by relatively scarce number of ATMs, we next utilized BM-derived macrophages (BMDMs) for further mechanistic studies. Compared to WT controls, basal expression of Tnf mRNA and of Il10 mRNA was consistently upregulated and downregulated, respectively in TKO BMDMs (Figure 3A and B and Figure 3—figure supplement 1A and B), recapitulating the above observations in ATMs. Notably, due to heightened expression of these cytokine genes in TLR-activated especially lipopolysaccharide (LPS)-stimulated conditions, upregulation or downregulation of Tnf and Il10 expression became more appreciable in activated macrophages than in resting cells (Figure 3C and D and Figure 3—figure supplement 1C–F). The mRNA expression patterns were further corroborated with alterations of TNF and IL-10 protein production by TKO macrophages (Figure 3E and F). Next, in order to clarify the connection between TNF and IL-10 in TKO BMDMs, we supplemented recombinant IL-10 protein prior to LPS stimulation and found that with sufficient quantity of IL-10, TNF production by TKO BMDMs reduced to a comparable level as WT BMDMs (Figure 3G). Moreover, blockade of IL-10 receptor with an anti-IL-10R antibody prior to LPS stimulation diminished the differences of TNF production observed between WT and TKO BMDMs (Figure 3—figure supplement 1G), implying that overexpression of TNF was likely secondary to inadequate amount of IL-10 produced by TKO cells. To functionally correct the obese phenotypes of TKO mice, an anti-TNF antibody or PBS was administrated weekly to TKO and WT mice along with HFD for 20 weeks. The anti-TNF treatment significantly reversed the obese phenotypes of TKO mice (Figure 3H), indicating that TNF was a functional target of miR-17~92 family miRNAs in regulating obesity. Overall, these results suggested that miR-17~92 family miRNAs suppressed TNF-mediated inflammation through promoting IL-10 expression in macrophages.

Figure 3. miR-17~92 family miRNAs balance the production of TNF and IL-10 in macrophages.

(A and B) qPCR analysis of Tnf (A) and Il10 (B) mRNA in WT and TKO BMDMs without stimulation. (C and D) qPCR analysis of Tnf (C) and Il10 (D) mRNA in WT and TKO BMDMs stimulated for the indicated periods with LPS. (E and F) ELISA of TNF (E) and IL-10 (F) in supernatants from WT and TKO BMDMs stimulated for the indicated periods with LPS. (G) ELISA of TNF in supernatants from WT and TKO BMDMs pre-treated 1 hr with indicated dose of IL-10 and then stimulated with LPS for 6 hr. (H) WT and TKO mice were injected with PBS or anti-TNF (10 mg/kg) weekly and were fed with a high-fat diet. Body weight of each mice was measured weekly. NS, not significant (p>0.05); *p<0.05, **p<0.01, and ***p<0.001 (paired Student’s t-test and two-way ANOVA). Data are representative of or are pooled from five (A–D), four (E and F), three (G), or one (H) independent experiments (mean + s.d. in A–G and mean ± sem in H).

Figure 3.

Figure 3—figure supplement 1. miR-17~92 family miRNAs regulate the production of TNF and IL-10 in macrophages.

Figure 3—figure supplement 1.

(A and B) qPCR analysis of Tnf (A) and Il10 (B) mRNA with an additional set of primers in WT and TKO BMDMs without stimulation. (C and D) qPCR analysis of Tnf (C) and Il10 (D) mRNA with an additional set of primers in WT and TKO BMDMs stimulated for the indicated periods with LPS. (E and F) qPCR analysis of Tnf (E) and Il10 (F) mRNA in WT and TKO BMDMs stimulated for the indicated periods with Pam3CSK4. (G) ELISA of TNF in supernatants from WT and TKO BMDMs pre-treated 0.5 hr with anti-IL-10R or anti-IgG (2 μg/ml) and then stimulated with LPS for 6 hr. NS, not significant (p>0.05); *p<0.05 (paired Student’s t-test). Data are representative of five (A–D) or two (E and F) (mean + s.d.) or are pooled from four (G) independent experiments (mean + s.e.m.).
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As miR-17~92 family miRNAs consist of three distinct clusters (Figure 1—figure supplement 1A), next we examined the relative contribution of each miRNA cluster in regulating TNF and IL-10 expression. Deletion of individual cluster or double deficiency of miR-106a~363 and miR-106b~25 clusters (DKO) did not significantly alter LPS-induced Tnf expression in macrophages (Figure 4A–D). Expression of Il10 showed modest reduction in individual cluster single KO as well as in DKO BMDMs (Figure 4E–H). To investigate the collective effects of these three clusters, we compared the fold changes of Tnf and Il10 expression over their respective WT controls among macrophages harboring various genotypes of miRNA deficiency and found that Tnf was only markedly upregulated upon deletion of all three clusters while the extent of Il10 downregulation largely correlated with the number of deleted miRNA clusters (Figure 4I and J). Taken together, these results indicated that three miRNA clusters of miR-17~92 family collectively inhibited TNF and promoted IL-10 expression in macrophages.

Figure 4. Three clusters of miR-17~92 family miRNAs regulate the expression of TNF and IL-10 collectively in macrophages.

Figure 4.

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(A–D) qPCR analysis of Tnf mRNA in WT and miR-17~92flox/flox Lyz2-Cre (17/92 KO) (A), miR-106a~363−/− (106a KO) (B), miR-106b~25−/− (106b KO) (C), or miR-106a~363−/− miR-106b~25−/− (106a 106b DKO) (D) BMDMs stimulated for the indicated periods with LPS. (E–H) qPCR analysis of Il10 mRNA in WT and 17/92 KO (E), 106a KO (F), 106b KO (G), or 106a 106b DKO (H) BMDMs stimulated for the indicated periods with LPS. (I and J) qPCR analysis of Tnf (I) and Il10 (J) mRNA in WT and various knockout (horizontal axes) BMDMs stimulated for 1 hr with LPS; results are presented relative to those of LPS-stimulated WT BMDMs. **p<0.01 (paired Student’s t-test). Data are representative of two (A–C and E–G) or three (D and H) independent experiments or are pooled from two to three (I and J) independent experiments (mean + s.d.).

miR-17~92 family miRNAs promote IL-10 production via sustaining Fos expression

Having established IL-10 as a key target of miR-17~92 family miRNAs-mediated regulation in macrophages, we then sought to identify the mechanisms by which they promote IL-10 expression. As phosphatase and tensin homolog (PTEN) is a well-studied target of miR-17~92 family miRNAs (O'Donnell et al., 2005; Xiao et al., 2008) and PTEN and its related molecules have been implicated in modulating IL-10 expression (Hu et al., 2006; Martin et al., 2005), we first investigated whether miR-17~92 family miRNAs regulated IL-10 and TNF through targeting PTEN. Inhibition of PTEN phosphatase activity by a chemical inhibitor in TKO BMDMs did not apparently alter expression of IL-10 and TNF, suggesting that the regulation of TNF and IL-10 by miR-17~92 family miRNAs was independent of PTEN (Figure 5—figure supplement 1A and B). To further explore how IL-10 was regulated by miR-17~92 family miRNAs, we performed genome-wide expression profiling analysis in TKO and WT BMDMs (Zhang et al., 2020). Interestingly, we found that Fos, encoding a key component of transcriptional factor AP-1 that drives the expression of IL-10 (Hu et al., 2006; Saraiva and O'Garra, 2010), was downregulated in TKO BMDMs compared to WT controls, whereas the expression of other reported targets of miR-17~92 family miRNAs remained minimally affected (Figure 5A). Then we experimentally confirmed that Fos was consistently and significantly decreased in TKO BMDMs at both mRNA and protein levels (Figure 5B–E and Figure 5—figure supplement 2A and B). In contrast to Fos, the expression of Jun, another key component of AP-1 complex, did not apparently differ between WT and TKO BMDMs (Figure 5—figure supplement 3A and B). Furthermore, overexpression of Fos in TKO BMDMs rescued the reduced expression of IL-10 and the enhanced expression of TNF to the levels comparable with that in WT cells (Figure 5F–H and Figure 5—figure supplement 4A and B), indicating that subdued expression of IL-10 in TKO BMDMs was likely resulted from diminished Fos activities. Thus, these data demonstrated that miR-17~92 family miRNAs promoted IL-10 production via sustaining the expression of Fos.

Figure 5. miR-17~92 family miRNAs promote the expression of Fos in macrophages.

(A) RNA-seq analysis showing RNA expression in TKO BMDMs versus those in WT cells. RNAs upregulated in TKO BMDMs were colored red, whereas RNAs downregulated were colored blue, gene Fos was pointed out and colored bright blue, and genes Phlpp2, E2f1, Pten, and Bim were pointed out and colored green. Top 10 upregulated genes (Atp6v0c-ps2, Hmga1-rs1, H2-Q6, H2-Ea-ps, Gm8580, Gm8909, Sap25, LOC547349, H2–l, and H2–Q10) were colored yellow and top 10 downregulated genes (Mir8114, 0610010B08Rik, Gm14430, Gm8615, Asb4, Gm14305, 6230416C02Rik, Gm38431, Pira7, and Cpt1b) were colored purple. (B and C) qPCR analysis of Fos mRNA in WT and TKO BMDMs stimulated for the indicated periods with LPS. (D) Immunoblotting analysis of c-Fos ad p38 (loading control) in whole-cell lysates of WT and TKO BMDMs treated for the indicated periods with LPS. (E) Quantifications of c-Fos protein abundance in unstimulated condition in (D) by densitometry from four independent experiments. (F) qPCR analysis of Il10 mRNA in WT and TKO BMDMs transfected with control or Fos overexpression vector and stimulated for the indicated periods with LPS. (G) Cumulative results from three independent experiments of Il10 levels in LPS-stimulated 6 hr results as in (F), normalized to mRNA expression in control vector transfected WT cells. (H) ELISA of IL-10 in supernatants from WT and TKO BMDMs transfected with control or Fos overexpression vector and stimulated with LPS for 12 hr. *p<0.05 and **p<0.01 (paired Student’s t-test). Data are representative of six (B), four (D), or three (F) independent experiments or are pooled from six (C), four (E), or three (G and H) independent experiments (mean + s.d.).

Figure 5.

Figure 5—figure supplement 1. The regulation of TNF and IL-10 by miR-17~92 family miRNAs is independent of PTEN.

Figure 5—figure supplement 1.

(A and B) qPCR analysis of Tnf (A) and Il10 (B) mRNA in WT and TKO BMDMs stimulated with or without LPS for 3 hr in the presence or absence of a PTEN inhibitor SF1670 (2 μM). Data are representative of three independent experiments (mean + s.d.).
Figure 5—figure supplement 2. miR-17~92 family miRNAs sustain Fos expression in TKO BMDMs.

Figure 5—figure supplement 2.

(A and B) The whole immunoblots of c-Fos (A) and p38 (B) in whole-cell lysates of WT and TKO BMDMs treated for the indicated periods with LPS.
Figure 5—figure supplement 3. The expression of Jun is comparable between WT and TKO BMDMs.

Figure 5—figure supplement 3.

(A) qPCR analysis of Jun mRNA in WT and TKO BMDMs stimulated for the indicated periods with LPS. (B) Immunoblotting analysis of c-Jun and p38 (loading control) in whole-cell lysates of WT and TKO BMDMs treated for the indicated periods with LPS. Data are representative of two independent experiments (mean + s.d.).
Figure 5—figure supplement 4. Overexpression of Fos reduces the expression of TNF in TKO BMDMs.

Figure 5—figure supplement 4.

(A and B) qPCR analysis of Fos (A) and Tnf (B) mRNA in WT and TKO BMDMs transfected with control or Fos overexpression vector and stimulated with or without LPS for 6 hr. Data are representative of three independent experiments (mean + s.d.).
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miR-17~92 family miRNAs directly target YY1 to regulate Fos expression

Having demonstrated that miR-17~92 family miRNAs regulate IL-10 by sustaining the expression of Fos, we next sought to investigate how Fos was regulated by miR-17~92 family miRNAs. In previous studies, Fos is reported to be inhibited by a transcriptional factor YY1 (Shi et al., 1997). Interestingly, despite the fact that the mRNA levels of Yy1 were comparable between WT and TKO BMDMs, YY1 protein was significantly increased in TKO BMDMs (Figure 6A–C), implying post-transcriptional regulation of YY1 by miR-17~92 family miRNAs. Bioinformatics analysis with the miRanda tool (Betel et al., 2010) revealed YY1 as a predicted target of the miR-17 family miRNAs, a subfamily of miR-17~92 family miRNAs (Figure 6D). Luciferase reporter assays with the constructs containing Yy1 3′UTR showed significant attenuation of luciferase activities by miRNAs from miR-17~92, miR-106a~363, or miR-106b~25 cluster, whereas mutation of the miR-17 family miRNAs’ binding sites on Yy1 3’UTR reversed such inhibitory effects (Figure 6E), suggesting that YY1 was likely to be the direct target of miR-17~92 family miRNAs. To further validate the involvement of YY1 in miR-17~92 family miRNAs-mediated regulation of Fos, we knocked down YY1 expression by RNA interference in WT and TKO BMDMs (Figure 6—figure supplement 1A–C). Knocking down YY1 significantly upregulated the expression of Fos at the mRNA and protein levels (Figure 6F–H and Figure 6—figure supplement 2A and B) and subsequently decreased TNF and increased IL-10 expression (Figure 6I–L). Collectively, these results suggested that miR-17~92 family miRNAs promoted Fos expression by releasing YY1-mediated inhibition.

Figure 6. miR-17~92 family miRNAs target YY1 to promote Fos expression in macrophages.

(A) qPCR analysis of Yy1 mRNA in WT and TKO BMDMs stimulated for the indicated periods with LPS. (B) Immunoblotting analysis of YY1 and p38 (loading control) in whole-cell lysates of WT and TKO BMDMs treated for the indicated periods with LPS. (C) Quantifications of YY1 protein abundance in unstimulated condition in (B) by densitometry from three independent experiments. (D) Schematic illustration of predicted binding sites of miR-17 family miRNAs (red) in wild-type (YY1-WT) or mutated (YY1-Mut) Yy1 3′ untranslated region (UTR). (E) Luciferase reporter assays of Rluc gene expression containing the wild-type or mutated binding sites (as in D) in Yy1 3′UTR in 293 T cells co-transfected with the luciferase reporter vector and negative control (Control), miR-17~92 cluster (17/92), miR-106a~363 cluster (106a), or miR-106b~25 cluster (106b) miRNAs overexpression vector. Results are presented as Rluc/Luc activity ratio and are normalized to values in the control vector group. (F) qPCR analysis of Fos mRNA in TKO BMDMs transfected with control or YY1 short interfering RNAs (siRNAs), normalized to mRNA expression in control siRNA transfected cells. (G) Immunoblotting analysis of c-Fos and p38 (loading control) in whole-cell lysates of TKO BMDMs transfected with control or YY1 siRNAs. (H) Quantifications of c-Fos protein abundance in (G) by densitometry from four independent experiments. (I and J) qPCR analysis of Tnf (I) and Il10 (J) mRNA in TKO BMDMs transfected with control or YY1 siRNAs and stimulated with LPS for 1 hr. (K and L) ELISA of TNF (K) and IL-10 (L) in supernatants from TKO BMDMs transfected with control or YY1 siRNAs and stimulated with LPS for 6 hr. *p<0.05 and **p<0.01 (paired Student’s t-test). Data are representative of three (A, B, I, and J) or four (G) independent experiments or are pooled from three (C, K, and L), six (E), or four (F and H) independent experiments (mean + s.e.m.).

Figure 6.

Figure 6—figure supplement 1. YY1 expression is efficiently knocked down by RNA interference.

Figure 6—figure supplement 1.

(A) qPCR analysis of Yy1 in WT BMDMs transfected with control or YY1 siRNAs, normalized to mRNA expression in control siRNA transfected cells. (B and C) qPCR analysis of Yy1 (B) and immunoblotting analysis of YY1 and p38 (loading control, C) in TKO BMDMs transfected with control or YY1 siRNAs, qPCR results were normalized to mRNA expression in control siRNA transfected cells. **p<0.01 (paired Student’s t-test). Data are pooled from (A) or are representative of (B) four independent experiments (mean + s.d.).
Figure 6—figure supplement 2. Fos is upregulated after knocking down YY1 in WT BMDMs.

Figure 6—figure supplement 2.

(A and B) qPCR analysis of Fos (A) and immunoblotting analysis of c-Fos and p38 (loading control, B) in WT BMDMs transfected with control or YY1 siRNAs. Data are representative of two independent experiments (mean + s.d.).
Figure 6—figure supplement 3. A model depicting functions and mechanisms of action of miR-17~92 family miRNAs in adipose tissue macrophages.

Figure 6—figure supplement 3.

MicroRNAs of miR-17~92 family target YY1 to release its repression on Fos, allowing optimal production of IL-10 and subsequently repress production of TNF, thus inhibiting chronic inflammation such as obesity. Overall, miR-17~92 family miRNAs act as crucial regulators of the balance between pro- and anti-inflammatory cytokines in macrophages to maintain adipose tissue homeostasis.
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Discussion

In the immune system, miR-17~92 family miRNAs have been predominantly studied in adaptive immune cells including T cells and B cells. However, the functions of miR-17~92 family miRNAs in innate immune cells remain elusive. Here we illustrated the previously unidentified functions as well as underlying mechanisms of miR-17~92 family miRNAs in regulating the balance between key pro- and anti-inflammatory mediators in macrophages and uncovered a novel target of miR-17~92 family miRNAs (Figure 6—figure supplement 3). Such regulation bears important biological outcomes in the context of macrophage-mediated chronic inflammation with impact on general physiology such as body weight and glucose tolerance.

In recent years, due to life style changes such as increased intake of refined foods, the incidence of obesity has risen dramatically all over the globe, which in turn leads to an explosion of obesity-related disorders including but not limited to insulin resistance and type 2 diabetes, fatty liver disease, and cardiovascular diseases (Finucane et al., 2011; González-Muniesa et al., 2017). A variety of factors have been implicated in the development of obesity, including dysregulation of interconnected endocrine circuits, systemic chronic inflammation, and certain cell-intrinsic mechanisms such as oxidative stress, mitochondrial dysfunction, and endoplasmic reticulum (ER) stress (Hotamisligil, 2006; Jaitin et al., 2019; Kahn and Flier, 2000; Qatanani and Lazar, 2007). Immune modulating cytokines including TNF and IL-10 are also critical regulators of insulin signaling (Chawla et al., 2011; Olefsky and Glass, 2010). For example, TNF-activated signaling molecules such as IKK, JNK, S6 kinase, and mammalian target of rapamycin (mTOR) could potentially phosphorylate insulin receptor substrate 1 (IRS1) to attenuate insulin signaling and subsequently contribute to the development of insulin resistance (Gao et al., 2002; Gao et al., 2003; Hirosumi et al., 2002; Lee et al., 2007; Ozes et al., 2001; Yin et al., 1998). Although miR-17~92 family miRNAs have been reported to regulate adipocyte development (Chen et al., 2014; Wang et al., 2008), the current study depicted a miR-17~92 family miRNA-centric immune circuit operative in macrophages that indirectly controls metabolic phenotypes such as insulin resistance and obesity.

Previous studies have implicated the roles of miR-17~92 family miRNAs in developmental processes and tumorigenesis (Mendell, 2008; Mogilyansky and Rigoutsos, 2013), and validated multiple targets including E2F1 (O'Donnell et al., 2005), PTEN (O'Donnell et al., 2005; Xiao et al., 2008), Bim (Ventura et al., 2008; Xiao et al., 2008), PHLPP2 (Kang et al., 2013), and C/EBPβ (Kang et al., 2020). In this study we identified YY1 as a novel target of miR-17~92 family miRNAs. Attenuation of YY1 protein by macrophage-intrinsic miR-17~92 family miRNAs sustains expression of Fos and thus activities of a key transcription complex AP-1, resulting in optimal expression of a major homeostatic cytokine IL-10 and restraining cells from overactivation. To our knowledge, these results represent the first experimental evidence for suggesting an immune-regulatory role of YY1 in myeloid cells. The detailed analyses of regulation and function of transcription factor YY1 in macrophages warrant future investigation. Nevertheless, despite identifying YY1 as a target of miR-17~92 family miRNAs, we cannot exclude the possibility that other miRNA targets may be involved in regulating macrophage phenotypes. Further studies are needed to fully elucidate the mechanisms by which miR-17~92 family miRNAs regulate macrophage biology.

TNF is a pro-inflammatory cytokine critical for host defense against bacterial, viral, and parasitic infections, yet excessive production of TNF has been causally linked with multiple autoimmune and inflammatory diseases such as rheumatoid arthritis, IBD, and diabetes (Bradley, 2008; Brenner et al., 2015). Thus, the production of TNF must be tightly controlled by a variety of negative regulators. IL-10 is a potent anti-inflammatory cytokine that plays a central role in resolution of inflammation and robustly suppresses production of inflammatory mediators such as TNF via transcriptional and post-transcriptional mechanisms (Moore et al., 2001; Ouyang and O'Garra, 2019). Full-fledged expression of IL-10 by macrophages is achieved by coordinated action of transcription factors including c-Maf and AP-1 (Cao et al., 2005; Ouyang et al., 2011; Saraiva and O'Garra, 2010). Here we identified miR-17~92 family miRNAs as key positive regulators supporting IL-10 production in ATMs, raising possibilities for this family of miRNAs as potential therapeutic targets of inflammatory and metabolic disorders.

Materials and methods

Key resources table.

Reagent type
(species) or resource		 Designation Source or reference Identifiers Additional
information
Genetic reagent (Mus. musculus) Lyz2-Cre The Jackson Laboratory Stock No: 004781
Genetic reagent (Mus. musculus) C57BL/6J The Jackson Laboratory Stock No: 000664
Genetic reagent (Mus. musculus) miR-106a~363−/− (Mirc2−/−) The Jackson Laboratory Stock No: 008461
Genetic reagent (Mus. musculus) miR-106b~25−/−(Mirc3−/−) The Jackson Laboratory Stock No: 008460
Genetic reagent (Mus. musculus) miR-17~92flox/flox (Mirc1flox/flox) The Jackson Laboratory Stock No: 008458
Genetic reagent (Mus. musculus) miR-106a~363−/− miR-106b~25−/− (Mirc2−/− Mirc3−/−) This paper N/A
Genetic reagent (Mus. musculus) miR-106a~363−/−miR-106b~25−/−miR-17~92flox/flox Lyz2-Cre (Mirc2−/− Mirc3−/− Mirc1flox/flox Lyz2-Cre) This paper N/A
Genetic reagent (Mus. musculus) CD45.1 Gift from Dr. Yan Shi, Tsinghua University N/A
Genetic reagent (Mus. musculus) Lyz2-RFP Gift from Dr. Yuncai Liu, Tsinghua University N/A
Cell line (Homo sapiens) 293T ATCC ACS-4500
Antibody Anti-mouse F4/80-APC (rat monoclonal) eBioscience Cat# 17-4801-82,
RRID:AB_2784648 		1:400
Antibody Anti-mouse CD45-APC/Cy7 (rat monoclonal) BioLegend Cat# 103116, RRID:AB_312981 1:400
Antibody Anti-mouse CD11b-PerCP-Cy5.5 (rat monoclonal) eBioscience Cat# 45-0112-82,
RRID:AB_953558 		1:400
Antibody Anti-mouse Mer-BV605 (rat monoclonal) BD Biosciences Cat# 747894 1:400
Antibody Anti-mouse CD64-PE (mouse monoclonal) BioLegend Cat# 139304, RRID:AB_10612740 1:200
Antibody Anti-mouse CD11c-FITC (hamster monoclonal) BD Biosciences Cat# 553801, RRID:AB_395060 1:400
Antibody Anti-mouse CX3CR1-PE/Cy7 (mouse monoclonal) BioLegend Cat# 149015, RRID:AB_2565699 1:400
Antibody Anti-mouse Ly6C-Alexa Fluor 700 (rat monoclonal) BioLegend Cat# 128024, RRID:AB_10643270 1:400
Antibody Anti-mouse CD45.1-BV421(mouse monoclonal) BioLegend Cat# 110732, RRID:AB_2562563 1:400
Antibody Anti-mouse CD45.2-PE/Cy7 (mouse monoclonal) eBioscience Cat# 25-0454-80, RRID:AB_2573349 1:400
Antibody p38α (C-20) (rabbit polyclonal) Santa Cruz Biotechnology Cat# sc-535, RRID:AB_632138 1:1000
Antibody c-Fos Antibody (4) (rabbit polyclonal) Santa Cruz Biotechnology Cat# sc-52, RRID:AB_2106783 1:1000
Antibody c-Jun (60A8) (rabbit monoclonal) Cell Signaling Technology Cat# 9165, RRID:AB_2130165 1:1000
Antibody YY1 antibody [EPR4652] (rabbit monoclonal) Abcam Cat# ab109237, RRID:AB_10890662 1:1000
Antibody Anti-mouse IL-10R (rat monoclonal) Bio X Cell Cat# BE0050, RRID:AB_1107611
Antibody Rat IgG1 isotype control (rat monoclonal) Bio X Cell Cat# BE0088, RRID:AB_1107775
Antibody Anti-mouse TNFα (rat monoclonal) Bio X Cell Cat# BP0058, RRID:AB_1107764
Recombinant DNA reagent psiCHECK-2-YY1-WT (plasmid) This paper N/A
Recombinant DNA reagent psiCHECK-2-YY1-Mut (plasmid) This paper N/A
Sequence-based reagent YY1 siRNA GenePharma N/A GAACUCACCUCCUGAUUAU (sense), AUAAUCAGGAGGUGAGUUC (antisense)
Sequence-based reagent Primers for qPCR This paper N/A see Supplementary file 1
Peptide, recombinant protein Recombinant Murine IL-10 PeproTech Cat# 210–10
Commercial assay or kit Total RNA Miniprep Purification Kit GeneMark TR01-150
Commercial assay or kit Mouse TNF ELISA Set BD Biosciences Cat# 555268
Commercial assay or kit IL-10 ELISA Kit BioLegend Cat# 431414
Commercial assay or kit TaqMan microRNA Reverse Transcription Kit Applied Biosystems Cat# 4366596
Commercial assay or kit TaqMan MicroRNA assays Applied Biosystems Cat# 4427975
Commercial assay or kit Dual-Luciferase Reporter Assay System Promega Cat# E1910
Chemical compound, drug SF1670 Selleck Cat# S7310
Chemical compound, drug LPS-EB (LPS from E. coli O111:B4) InvivoGen Cat# tlrl-eblps
Chemical compound, drug Pam3CSK4 InvivoGen Cat# tlrl-pms
Chemical compound, drug Lipofectamine 2000 Transfection Reagent Invitrogen Cat# 11668027
Chemical compound, drug TransIT-TKO Transfection Reagent Mirus Cat# MIR2150
Software, algorithm GraphPad Prism GraphPad Software RRID:SCR_002798
Software, algorithm FlowJo(v10.0.7) FlowJo RRID:SCR_008520
Software, algorithm Image J (v1.52a) https://imagej.nih.gov/ij/index.html RRID:SCR_003070
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Mice

The miR-106a~363−/− (Mirc2−/−, Jax stock #008461), miR-106b~25−/− (Mirc3−/−, Jax stock #008460), and miR-17~92flox/flox (Mirc1flox/flox, Jax stock #008458) mice were purchased from The Jackson Laboratory, which were all on C57BL/6J background. miR-17~92flox/flox were crossed with Lyz2-Cre mice to obtain mice with myeloid-specific deletion of miR-17~92 cluster. The miR-106a~363 and miR-106b~25 miRNA clusters double KO mice were obtained by crossing miR-106a~363−/− mice with miR-106b~25−/− mice. The miR-17~92, miR-106a~363, and miR-106b~25 clusters triple KO mice were obtained by crossing miR-106a~363−/− miR-106b~25−/− mice with miR-17~92flox/flox Lyz2-Cre mice. CD45.1 mice were kindly provided by Dr. Yan Shi (Tsinghua University). Lyz2-RFP mice were kindly provided by Dr. Yuncai Liu (Tsinghua University) which were originally bred from Lyz2-Cre and ROSA-tdRFP mice (Luche et al., 2007). Experiments were performed with mice at 6–8 weeks of age unless otherwise specified with age- and gender-matched controls. Wild-type C57BL/6J mice were used as controls for miR-106a~363−/−, miR-106b~25−/−, and miR-106a~363−/− miR-106b~25−/− mice, and Lyz2-Cre mice were used as controls for miR-17~92flox/flox Lyz2-Cre and miR-106a~363−/− miR-106b~25−/− miR-17~92flox/flox Lyz2-Cre mice. All experiments using mice were approved by the Institutional Animal Care and Use Committees at Tsinghua University (Protocol #17-HXY1).

Cell culture and reagents

Murine BMDMs were obtained as previously described (Xu et al., 2012) and maintained in DMEM (HyClone) supplemented with 10% FBS (Gibco) and 10% supernatant of L929 cell as conditioned medium providing macrophage colony-stimulating factor (M-CSF, identified as complete medium). Briefly, bone marrow cells were extracted and cultured with complete medium for 5 days to derive BMDMs. Cell culture grade LPS and Pam3CSK4 were purchased from Invivogen and were used at a concentration of 10 ng/ml unless otherwise specified. SF1670 were from Selleck. anti-IL10R, anti-IgG, and anti-TNF were purchased from BioXCell. anti-IL10R or anti-IgG was added 30 min prior to LPS stimulation and were present throughout LPS exposure.

Reverse transcription and qPCR

RNA was extracted from whole cell lysates and was reversely transcribed to cDNA as previously described (Zhang et al., 2019). qPCR was performed with an ABI StepOnePlus thermal cycler. Primary transcripts were measured with primers that amplify either exon–intron junctions or intronic sequences. Threshold cycle numbers were normalized to samples amplified with primers specific for glyceraldehyde-3-phosphate dehydrogenase (Gapdh). For qPCR analysis of mature miRNA, cDNA was prepared from total RNA, which was isolated with TRIzol reagent (Invitrogen), with the TaqMan microRNA Reverse Transcription Kit (Applied Biosystems). TaqMan MicroRNA assays were used according to the manufacturer’s recommendations (Applied Biosystems) for real-time PCR. Expression of U6 snRNA was used for normalization of expression values. Primer sequences are listed in Supplementary file 1.

Enzyme-linked immunosorbent assay (ELISA)

Cytokine secretion was quantified with TNF (BD Biosciences) or IL-10 (Biolegend) ELISA kit according to the manufacturers' instructions.

BM chimeras

BM chimeras were generated as previously described (Hu et al., 2008). Briefly, 6-week-old recipient C57BL/6J mice or CD45.1 mice were irradiated twice at a dose of 5.5 Gy with 3 hr break in between, followed by intravenous injection of 106 donor BM cells from WT or TKO mice. Chimeric mice were used for experiments 6 weeks after the initial BM transfer.

HFD-induced obesity

Mice were fed with an HFD and body weight was measured weekly for 13–28 weeks. Mice were scanned with MRI machine (QMR06-090H, NIUMAG) for analysis of body composition before scarification for VAT excision. VAT weight was measured and was calculated for the percentage of body weight.

Glucose tolerance test

Mice were injected intraperitoneally with glucose (2 mg/g of body weight) after fasting overnight (14–16 hr). The levels of blood glucose were measured with a glucometer (GA-3, SANNUO).

Isolation of cells from adipose tissue

Mice were sacrificed and VAT were removed and cut into small pieces. Then tissues were digested in 5 ml ice cold digestion buffer (RPMI 1640 medium containing 10% FBS, 2 mg/ml Collagenase IV [Sigma-Aldrich], and 50 μg/ml DNaseI [Sigma-Aldrich]) for 45 min at 37°C with rotation (200 rpm). The digested tissues were passed through a 70 μm cell strainer and centrifugated at 1500 rpm for 10 min to get the isolated cells.

Flow cytometry

Cells from adipose tissues were prepared and lysed with ACK lysing buffer (Gibco) to exclude red blood cells and were stained with antibodies on ice for 30 min in the dark. APC/Cy7 anti-mouse CD45 (30-F11, BioLegend), Percpcy5.5 anti-mouse CD11b (M1/70, eBioscience), and APC anti-mouse F4/80 (BM8, eBioscience) were used to stain ATMs, and BV605 anti-mouse Mer (108928, BD Biosciences), PE anti-mouse CD64 (X54-5/7.1 BioLegend), FITC anti-mouse CD11c (HL3, BD Biosciences), PE/Cy7 anti-mouse CX3CR1 (SA011F11, BioLegend), and Alexa Fluor 700 anti-mouse Ly6C (HK1.4, BioLegend) were used to further analyze surface markers of ATMs, BV421 anti-mouse CD45.1 (A20, BioLegend) and PE/Cy7 anti-mouse CD45.2 (104, eBioscience) were used to identify ATMs from CD45.1 and CD45.2 mice, and all antibodies were used in 1:400 dilutions except CD64 (used in 1:200 dilutions). Cells were washed three times and they were performed on FACSFortessa or FACSAria III flow cytometer (BD Biosciences) and analyzed with FlowJo software (Tree Star).

Immunoblotting analysis

Whole cell lysates were prepared as described previously (Xu et al., 2012). For immunoblotting analysis, lysates were separated by 10% SDS-PAGE and transferred to a PVDF membrane (Millipore) for probing with antibodies. The antibodies against p38 (sc-535) and c-Fos (sc-52) were purchased from Santa Cruz Biotechnology, YY1 antibody (ab109237) was purchased from Abcam, and c-Jun antibody (#9165) was obtained from Cell Signaling Technology.

RNA sequencing and analysis

Total RNA was isolated with TRIzol reagent (Invitrogen) from whole cell lysates of ATMs from WT and TKO mice fed with regular chow diet or HFD. RNA was isolated, libraried, and sequenced with Illumina Novaseq platform by Novogene (Novogene; Beijing, China). Total reads were cleaned and mapped to the mm10 reference genome and then were normalized as fragments per kilobase of transcript per million mapped reads (FPKM). Upregulated and downregulated genes in TKO ATMs were identified as p≤0.05, (FPKM +1) fold changes (TKO/WT) ≥1.6 for upregulated genes, and (FPKM +1) fold changes (TKO/WT) ≤0.6 for downregulated genes. Differential expressed genes (DEGs) between WT-HFD and WT ATMs were defined as p≤0.05 and absolute value of log2 ratio (WT-HFD/WT) ≥1. GO analysis was conducted with upregulated and downregulated genes in TKO versus WT ATMs group and DEGs in WT-HFD versus WT ATM group by Enrichr (Chen et al., 2013; Kuleshov et al., 2016). Inflammatory score was calculated with the following formula: Inflammatory score = sum of log (FPKM+1) of representative pro-inflammatory genes (Tnf, Il6, Cxcl1, Ccl2, and Ccl8) − sum of log (FPKM+1) of representative anti-inflammatory genes (Il10, Il4, Il5, and Il13). RNA-seq datasets with WT and TKO BMDMs from our previous published work (Zhang et al., 2020) were reanalyzed and plotted.

Luciferase reporter assay

The psiCHECK2 (Promega) reporter plasmid was cloned with 3′-UTR fragments of Yy1 containing wild-type or mutated predicted binding sites of miR-17 family miRNAs (Supplementary file 2) to generate Yy1 reporter plasmids. A total of 293 T cells were plated into 24-well plates at 1 × 105 cells per well 24 hr before transfection with 10 ng reporter plasmid and 500 ng overexpression vectors encoding miRNAs of miR-17~92, miR-106a~363, or miR-106b~25 clusters using the Lipofectamine 2000 transfection reagent (Invitrogen). Luciferase assays were performed 48 hr post-transfection using the Dual-Luciferase Reporter Assay System (Promega) following the manufacturer’s protocol. The renilla firefly luciferase (Rluc) activity was normalized by the firefly luciferase activity (Luc) and expression is presented as Rluc/Luc ratio.

Cell lines

A total of 293 T cells (ATCC ACS-4500) were maintained in DMEM supplemented with 10% FBS and 1% Penicillin–Streptomycin (Gibco) and tested negative for mycoplasma.

RNA-mediated interference

Small interfering RNA (siRNA) specifically targeting mouse Yy1 (5′-GAACU CACCU CCUGA UUAU-3′ (sense)/5′-AUAAU CAGGA GGUGA GUUC-3′ (antisense)) and nontargeting control siRNA were from GenePharma. The siRNAs were transfected into mouse BMDMs through the use of TransIT TKO transfection reagent according to the manufacturer’s instructions (Mirus Bio). Cells were then lysed for mRNA and protein extraction 48 hr post-transfection.

Statistical analysis

p-values were calculated with a two-tailed paired or unpaired Student’s t-test or two-way ANOVA. Not significant, p>0.05; *p<0.05; **p<0.01; ***p<0.001; and ****p<0.0001.

Data and materials availability

All data supporting the findings of this study are presented within the article and its supplementary files. The RNA-seq data set is deposited in the National Center for Biotechnology Information Gene Expression Omnibus under accession number GSE129613 (Zhang et al., 2020) and GSE158627.

Acknowledgements

We thank Y Zhang (University of Maryland) and B Zhang (Tsinghua University) for help with RNA-seq analysis and Y Shi (Tsinghua University) and Y Liu (Tsinghua University) for kindly providing several mouse strains. This research was supported by National Natural Science Foundation of China grants (31725010 and 31821003 to XH and 31330027 to LW), funds from Tsinghua-Peking Center for Life Sciences (XZ, XH, and LW), and funds from Institute for Immunology at Tsinghua University (XH and LW).

Funding Statement

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

Contributor Information

Xiaoyu Hu, Email: xiaoyuhu@tsinghua.edu.cn.

Florent Ginhoux, Agency for Science Technology and Research, Singapore.

Satyajit Rath, Indian Institute of Science Education and Research (IISER), India.

Funding Information

This paper was supported by the following grants:

  • National Natural Science Foundation of China 31821003 to Xiaoyu Hu.

  • National Natural Science Foundation of China 31725010 to Xiaoyu Hu.

  • National Natural Science Foundation of China 31330027 to Li Wu.

  • Tsinghua-Peking Center for Life Science to Xiang Zhang, Li Wu, Xiaoyu Hu.

  • Institute for Immunology at Tsinghua University to Li Wu, Xiaoyu Hu.

Additional information

Competing interests

No competing interests declared.

Author contributions

Conceptualization, Data curation, Formal analysis, Validation, Methodology, Writing - original draft, Writing - review and editing.

Resources.

Resources.

Conceptualization, Supervision, Funding acquisition, Investigation, Writing - original draft, Project administration, Writing - review and editing.

Ethics

Animal experimentation: All experiments using mice were approved by the Institutional Animal Care and Use Committees at Tsinghua University (Protocol #17-HXY1).

Additional files

Supplementary file 1. Primers used in this study.
elife-55676-supp1.docx (13.4KB, docx)
Supplementary file 2. Yy1 3′UTR cloned fragments with miR-17 family miRNAs binding sites for luciferase reporter assays.
elife-55676-supp2.docx (13.2KB, docx)
Transparent reporting form

Data availability

Sequencing data have been deposited in GEO under accession code GSE129613 and GSE158627.

The following datasets were generated:

Zhang X. 2019. RNAseq to profile transcriptomes in bone marrow-derived macrophages from TKO(mir-106a~363-/- mir-106b~25-/- mir-17~92flox/flox Lyz2-Cre) and WT(Lyz2-Cre) mice. NCBI Gene Expression Omnibus. GSE129613

Zhang X. 2020. RNAseq to profile transcriptomes in adipose tissue macrophages (ATMs) from TKO(mir-106a~363-/- mir-106b~25-/- mir-17~92flox/flox Lyz2-Cre) and WT(Lyz2-Cre) mice fed with regular chow diet or high fat diet. NCBI Gene Expression Omnibus. GSE158627

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Decision letter

Editor: Florent Ginhoux1

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

Acceptance summary:

Your study identified novel roles of miR-17~92 family miRNAs in regulating macrophage inflammatory phenotypes. Disruption of the miR-17~92 family leads to increased pro-inflammatory activity of adipose macrophages. Myeloid specific deletion of three clusters of miR-17~92 family miRNAs pushed adipose tissue macrophages towards a spontaneously inflammatory phenotype characterized by diminished IL-10 and elevated TNF, leading to disrupted adipose homeostasis evidenced by obesity and compromised glucose tolerance. Mechanistically, miR-17~92 family miRNAs prevented inflammation by promoting IL-10 expression through a YY1-Fos-IL-10 regulatory circuit.

Decision letter after peer review:

Thank you for submitting your article "MicroRNAs of the miR-17~92 family maintain adipose tissue macrophage homeostasis by sustaining IL-10 expression" for consideration by eLife. Your article has been reviewed by Satyajit Rath as the Senior Editor, a Reviewing Editor, and two reviewers. The reviewers have opted to remain anonymous.

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

Summary:

The results presented here identified novel roles of miR-17~92 family miRNAs in regulating macrophage inflammatory phenotypes. The authors show that disruption of the miR-17~92 family leads to increased pro-inflammatory activity of adipose macrophages. Myeloid specific deletion of three clusters of miR-17~92 family miRNAs altered twisted adipose tissue macrophages towards a spontaneously inflammatory phenotype characterized by diminished IL-10 and elevated TNF, leading to disrupted adipose homeostasis evidenced by obesity and compromised glucose tolerance. Mechanistically, miR-17~92 family miRNAs prevented inflammation by promoting IL-10 expression through a YY1-Fos-IL-10 regulatory circuit. Overall, this is an interesting study (especially the fact that miR-17~92 family KO mice are "obese") with strong physiological significance and compelling molecular mechanisms. However, more experiments are needed to support a clear link between the inflammatory status observed in miR-17~92 family KO mice and adipose macrophages.

Essential revisions:

The majority of experiments was performed with BM-derived macrophages cultured for an unknown time in MCSF media. However, monocyte-derived macrophages are significantly different compared to tissue macrophages and show similarities – at the utmost – to peritoneal infiltrates after thioglycolate injection, but not to homeostatic tissue-resident macrophages (Gosselin et al., 2014). It is therefore questionable if the here presented findings on (partially LPS-stimulated) BMDMs can be translated to tissue-resident adipose macrophages under physiological conditions. It is similarly possible that the development of exacerbated weight gain in TKO is the consequence of a peripheral phenotype (for instance of monocytic origin), as indicated by the BM transplantation experiments, rather than the consequence of a defect in tissue-resident macrophages. Accordingly, the authors need to perform more experiments to show that the phenotype is caused by adipose macrophages. For example, showing the expression levels of all miR-17~92 family members in ex vivo isolated ATM subpopulations. In this regard, am important study to consider will be Jaitin et al., 2019.

It is further not clear, why the authors included LPS stimulation in their experimental design, when the phenotype is evident in TKO mice under physiological conditions. It would be easier for the reader, if the authors would focus on unstimulated cells rather than showing different time points after LPS treatment. Results from the in vitro LPS experiments cannot be extrapolated to the physiological in vivo situation. Finally, to overcome the correlative nature of the experiments, it would be necessary to show, that the "obese" phenotype can be rescued in TKO Tnf+/- mice.

The authors showed that IL-10 supplementation can rescue the TNF phenotypes in miR-17~92-deficient microphages. It will be important to further extend this result by using an IL-10 blocking system. On a similar point, in respect to Figure 6, the link between YY1-Fos-Il10 is not completely clear. The authors should show that silencing of YY1 in TKO leads to decreased Tnf and increased Il10 expression. The authors should also test the effects of YY1 loss on cytokine production?

In Figure 1D the authors show body weight during HFD and indicate higher weight of TKO. However, in Figure 1A the authors show that TKO already have higher weight under steady state conditions. The authors need to add the weights of untreated WT and TKO to the graph. Same for Figure 1I, the authors should show the weight of chimeras without HFD.

RNA-seq experiment should be performed with ex vivo isolated ATM and not with in vitro derived cells. Two time points for both genotypes are necessary ("pre-obese" and "obese") to observe gene changes during obesity development.

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

Thank you for submitting your article "MicroRNAs of the miR-17~92 family maintain adipose tissue macrophage homeostasis by sustaining IL-10 expression" for consideration by eLife. Your article has been reviewed by Satyajit Rath as the Senior Editor, a Reviewing Editor, and two reviewers. The reviewers have opted to remain anonymous.

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

We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation.

Summary:

The authors significantly improved their work during the revision and strengthen their main points with new experimental evidence significantly. However, some few minor things remain to be clarified before we can proceed to acceptance.

Essential revisions:

1) The bioinformatical analysis or the description of the corresponding RNA-Seq experiments is still complicated to understand. The authors state in their new version: "Furthermore, genome wide RNA profiling analyses were performed with fluorescence-activated cell sorting (FACS) sorted ATMs from WT and TKO mice fed with regular chow diet or high fat diet." However, in the Figure legend it is stated: "(E) Gene Ontology analysis of WT and TKO ATM RNA-seq datasets showing the enriched gene ontology terms in TKO ATMs. (F) RNA-seq analysis showing RNA expression in TKO ATMs versus those in WT cells. RNAs up-regulated in TKO ATMs were colored red, whereas RNAs down-regulated were colored blue, gene Tnf was pointed out and colored orange and gene Il10 was pointed out and colored green." Is this data shown in 2E+F now derived from obese or pre-obese animals? If this is derived from just one condition (obese or pre-obese mice), why didn't the authors show all conditions?

2) Furthermore, how many animals were used for the RNA-Seq experiment depicted in Figure 2E and F? Is this just 2 samples (one WT and one TKO) pooled from multiple mice? Please either indicate in the figure legends or in the Materials and methods section.

3) The authors stated that red/blue marked genes in Figure 2F indicates up- or down-regulated genes. What is their definition for up- or down-regulation? 2-fold change? Or is this based on statistics (which might be problematic with n=1).

4) Please highlight the top 10 differential expressed genes in the Figure 2E and Figure 5A scatter blot.

eLife. 2020 Nov 5;9:e55676. doi: 10.7554/eLife.55676.sa2

Author response

[…]

Essential revisions:

The majority of experiments was performed with BM-derived macrophages cultured for a unknown time in MCSF media. However, monocyte-derived macrophages are significantly different compared to tissue macrophages and show similarities – at the utmost – to peritoneal infiltrates after thioglycolate injection, but not to homeostatic tissue-resident macrophages (Gosselin et al., 2014). It is therefore questionable if the here presented findings on (partially LPS-stimulated) BMDMs can be translated to tissue-resident adipose macrophages under physiological conditions. It is similarly possible that the development of exacerbated weight gain in TKO is the consequence of a peripheral phenotype (for instance of monocytic origin), as indicated by the BM transplantation experiments, rather than the consequence of a defect in tissue-resident macrophages. Accordingly, the authors need to perform more experiments to show that the phenotype is caused by adipose macrophages. For example, showing the expression levels of all miR-17~92 family members in ex vivo isolated ATM subpopulations. In this regard, am important study to consider will be Jaitin et al., 2019.

We thank the reviewer for raising these valid concerns regarding utilization of BMDMs and the contribution of ATMs versus peripheral monocytes. We used BMDMs mainly for the mechanistic studies due to the scarcity of the number of ATMs (approximately 40,000 cells from 0.1 g fat tissues per mouse). To corroborate the findings obtained from in vitro cultured macrophages, we ex vivo isolated ATMs and showed that miR-17~92 family miRNAs were efficiently deleted in ATMs from TKO mice (new Figure 2—figure supplement 2C-E, described in subsection “miR-17~92 family miRNAs maintain adipose tissue macrophage homeostasis”). Importantly, in ATMs, the expression of IL-10 and TNF was regulated by miR-17~92 family miRNAs in a similar trend to that in BMDMs (new Figure 2F-H, described in subsection “miR-17~92 family miRNAs maintain adipose tissue macrophage homeostasis”), further validating BMDMs as a suitable in vitro system for the mechanistic investigations. As the Lyz2-Cre line used in this study does not discriminate between monocytes and certain tissue resident macrophages, we cannot definitively exclude the effects of recruited monocytes, although the robust deletion of miR-17~92 family miRNAs as well as the inflammatory phenotypes of ATMs supported the notion that ATMs might contribute significantly to the overall phenotypes. We also included the Jaitin et al., reference in the revised manuscript.

It is further not clear, why the authors included LPS stimulation in their experimental design, when the phenotype is evident in TKO mice under physiological conditions. It would be easier for the reader, if the authors would focus on unstimulated cells rather than showing different time points after LPS treatment. Results from the in vitro LPS experiments cannot be extrapolated to the physiological in vivo situation. Finally, to overcome the correlative nature of the experiments, it would be necessary to show, that the "obese" phenotype can be rescued in TKO Tnf+/- mice.

In the absence of activating signals, resting BMDMs expressed nearly undetectable amounts of inflammatory genes such as Tnf at both mRNA and protein levels (threshold cycle number > 30 by qPCR for Tnf mRNA and protein concentrations < 5 pg/ml as measured by TNF ELISA). Thus, in order to reveal the effects of miR-17~92 family miRNAs and to perform mechanistic experiments in vitro, we used LPS stimulation to activate macrophages and to upregulate inflammatory gene expression. Of note, the results obtained from activated BMDMs are in large consistent with the ex vivo isolated ATMs regarding inflammatory gene regulation.

For the phenotype rescue experiment, we agree with the reviewer that TNF loss-of-function experiment would be of great value. However, incorporating a Tnf null allele into the current TKO background would result in knockin and knockout of 5 different gene alleles and the time to generate such mice might take one year, to the conservative estimate. Alternatively, we employed in vivo administration of an anti-TNF antibody and found that TNF blockade markedly ameliorated the obese phenotype in TKO animals (new Figure 3H, described in subsection “miR-17~92 family miRNAs balance TNF and IL-10 production in macrophages”). We believe that this approach is equally valuable at demonstrating a role for TNF in development of obesity in TKO animals, with the added benefits of boosting therapeutic potentials.

The authors showed that IL-10 supplementation can rescue the TNF phenotypes in miR-17~92-deficient microphages. It will be important to further extend this result by using an IL-10 blocking system. On a similar point, in respect to Figure 6, the link between YY1-Fos-Il10 is not completely clear. The authors should show that silencing of YY1 in TKO leads to decreased Tnf and increased Il10 expression. The authors should also test the effects of YY1 loss on cytokine production?

The expression of IL-10 is reduced in TKO macrophages compared to that in WT macrophages, and thus we supplemented IL-10 instead of blocking IL-10 signaling. Nevertheless, we performed the loss-of-function experiment with an anti-IL-10R antibody and found that blockade of IL-10 signaling normalized the difference of TNF production in WT and TKO BMDMs (new Figure 3—figure supplement 1G, described in subsection “miR-17~92 family miRNAs balance TNF and IL-10 production in macrophages”), further supporting a role for IL-10 in the system. Moreover, silencing YY1 in TKO BMDMs led to decreased TNF and increased IL10 expression at the mRNA and protein levels (new Figure 6I-L, described in subsection “miR-17~92 family miRNAs directly target YY1 to regulate Fos expression”), agreeing with the model of miR-17~92 family miRNAs-IL-10-TNF regulatory axis.

In Figure 1D the authors show body weight during HFD and indicate higher weight of TKO. However, in Figure 1A the authors show that TKO already have higher weight under steady state conditions. The authors need to add the weights of untreated WT and TKO to the graph. Same for Figure 1I, the authors should show the weight of chimeras without HFD.

We have added the weights of WT and TKO mice with regular chow diet (new Figure 1—figure supplement 3C, described in subsection “miR-17~92 family miRNAs protect mice from obesity”) as well as the weights of WT and TKO chimeras with regular chow diet (new Figure 1—figure supplement 3F, described in subsection “miR-17~92 family miRNAs protect mice from obesity”).

RNA-seq experiment should be performed with ex vivo isolated ATM and not with in vitro derived cells. Two time points for both genotypes are necessary ("pre-obese" and "obese") to observe gene changes during obesity development.

We thank the reviewer for suggesting this experiment. We have performed the RNA-seq experiment with ex vivo isolated ATMs from ‘pre-obese’ (regular chow diet) and ‘obese’ (high fat diet for 8 weeks) mice. Analyses of gene expression data sets revealed that ATMs from both ‘pre-obese’ and ‘obese’ TKO mice showed more prominent inflammatory gene signature than those from WT mice (new Figure 2E and F and new Figure 2—figure supplement 4A and B, described in subsection “miR-17~92 family miRNAs maintain adipose tissue macrophage homeostasis”), further confirming the inflammatory phenotypes of TKO ATMs.

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

[…]

Essential revisions:

1) The bioinformatical analysis or the description of the corresponding RNA-Seq experiments is still complicated to understand. The authors state in their new version: "Furthermore, genome wide RNA profiling analyses were performed with fluorescence-activated cell sorting (FACS) sorted ATMs from WT and TKO mice fed with regular chow diet or high fat diet." However, in the Figure legend it is stated: "(E) Gene Ontology analysis of WT and TKO ATM RNA-seq datasets showing the enriched gene ontology terms in TKO ATMs. (F) RNA-seq analysis showing RNA expression in TKO ATMs versus those in WT cells. RNAs up-regulated in TKO ATMs were colored red, whereas RNAs down-regulated were colored blue, gene Tnf was pointed out and colored orange and gene Il10 was pointed out and colored green." Is this data shown in 2E and F now derived from obese or pre-obese animals? If this is derived from just one condition (obese or pre-obese mice), why didn't the authors show all conditions?

In Figure 2E and F, these data were derived from pre-obese animals and a description was added in the figure legend. Those RNA-seq results from obese WT and TKO mice were shown in Figure 2—figure supplement 4A and B.

2) Furthermore, how many animals were used for the RNA-Seq experiment depicted in Figure 2E+F? Is this just 2 samples (one WT and one TKO) pooled from multiple mice? Please either indicate in the figure legends or in the Materials and methods section.

RNA from two mice of each genotype were pooled for RNA-seq experiment and this information was added in figure legend.

3) The authors stated that red/blue marked genes in Figure 2F indicates up- or down-regulated genes. What is their definition for up- or down-regulation? 2-fold change? Or is this based on statistics (which might be problematic with n=1).

In Figure 2F, upregulated and downregulated genes in TKO ATMs were identified as p-value ≤ 0.05, (FPKM +1) fold changes (TKO/WT) ≥ 1.6 for upregulated genes and (FPKM +1) fold changes (TKO/WT) ≤ 0.6 for downregulated genes, which was described in Materials and methods section.

4) Please highlight the top 10 differential expressed genes in the Figure 2E and Figure 5A scatter blot.

Those top 10 upregulated and downregulated genes were highlighted in new Figure 2E and new Figure 5A and were listed in figure legends.

Associated Data

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

    Data Citations

    1. Zhang X. 2019. RNAseq to profile transcriptomes in bone marrow-derived macrophages from TKO(mir-106a~363-/- mir-106b~25-/- mir-17~92flox/flox Lyz2-Cre) and WT(Lyz2-Cre) mice. NCBI Gene Expression Omnibus. GSE129613
    2. Zhang X. 2020. RNAseq to profile transcriptomes in adipose tissue macrophages (ATMs) from TKO(mir-106a~363-/- mir-106b~25-/- mir-17~92flox/flox Lyz2-Cre) and WT(Lyz2-Cre) mice fed with regular chow diet or high fat diet. NCBI Gene Expression Omnibus. GSE158627

    Supplementary Materials

    Supplementary file 1. Primers used in this study.
    elife-55676-supp1.docx (13.4KB, docx)
    Supplementary file 2. Yy1 3′UTR cloned fragments with miR-17 family miRNAs binding sites for luciferase reporter assays.
    elife-55676-supp2.docx (13.2KB, docx)
    Transparent reporting form
    elife-55676-transrepform.docx (246.4KB, docx)

    Data Availability Statement

    Sequencing data have been deposited in GEO under accession code GSE129613 and GSE158627.

    The following datasets were generated:

    Zhang X. 2019. RNAseq to profile transcriptomes in bone marrow-derived macrophages from TKO(mir-106a~363-/- mir-106b~25-/- mir-17~92flox/flox Lyz2-Cre) and WT(Lyz2-Cre) mice. NCBI Gene Expression Omnibus. GSE129613

    Zhang X. 2020. RNAseq to profile transcriptomes in adipose tissue macrophages (ATMs) from TKO(mir-106a~363-/- mir-106b~25-/- mir-17~92flox/flox Lyz2-Cre) and WT(Lyz2-Cre) mice fed with regular chow diet or high fat diet. NCBI Gene Expression Omnibus. GSE158627

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