Egr2 drives the differentiation of Ly6Chi monocytes into fibrosis-promoting macrophages in metabolic dysfunction-associated steatohepatitis in mice

Iwata, Ayaka; Maruyama, Juri; Natsuki, Shibata; Nishiyama, Akira; Tamura, Tomohiko; Tanaka, Minoru; Shichino, Shigeyuki; Seki, Takao; Komai, Toshihiko; Okamura, Tomohisa; Fujio, Keishi; Tanaka, Masato; Asano, Kenichi

doi:10.1038/s42003-024-06357-5

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Published: 03 June 2024

Egr2 drives the differentiation of Ly6C^hi monocytes into fibrosis-promoting macrophages in metabolic dysfunction-associated steatohepatitis in mice

Communications Biology volume 7, Article number: 681 (2024) Cite this article

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Abstract

Metabolic dysfunction-associated steatohepatitis (MASH), previously called non-alcoholic steatohepatitis (NASH), is a growing concern worldwide, with liver fibrosis being a critical determinant of its prognosis. Monocyte-derived macrophages have been implicated in MASH-associated liver fibrosis, yet their precise roles and the underlying differentiation mechanisms remain elusive. In this study, we unveil a key orchestrator of this process: long chain saturated fatty acid-Egr2 pathway. Our findings identify the transcription factor Egr2 as the driving force behind monocyte differentiation into hepatic lipid-associated macrophages (hLAMs) within MASH liver. Notably, Egr2-deficiency reroutes monocyte differentiation towards a macrophage subset resembling resident Kupffer cells, hampering hLAM formation. This shift has a profound impact, suppressing the transition from benign steatosis to liver fibrosis, demonstrating the critical pro-fibrotic role played by hLAMs in MASH pathogenesis. Long-chain saturated fatty acids that accumulate in MASH liver emerge as potent inducers of Egr2 expression in macrophages, a process counteracted by unsaturated fatty acids. Furthermore, oral oleic acid administration effectively reduces hLAMs in MASH mice. In conclusion, our work not only elucidates the intricate interplay between saturated fatty acids, Egr2, and monocyte-derived macrophages but also highlights the therapeutic promise of targeting the saturated fatty acid-Egr2 axis in monocytes for MASH management.

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Introduction

Non-alcoholic fatty liver disease, recently renamed as metabolic dysfunction-associated steatotic liver disease (MASLD)¹, is a spectrum of disorders characterized by excessive lipid accumulation in hepatocytes. MASLD progresses from fatty liver (steatosis) to metabolic dysfunction-associated steatohepatitis (MASH), and in more severe cases, to liver fibrosis². Although the prognosis of patients with MASLD is strongly associated with the degree of fibrosis^3,4, the mechanism underlying the transition from simple steatosis to fibrosis remains unclear, making the development of anti-MASH therapy a huge hurdle. Hepatic stellate cells (HSCs) and myofibroblasts that differentiate from HSCs are considered to play a predominant role in the progression to liver fibrosis⁵. Recently, not only such stromal cells but also immune cells, macrophages in particular, are found to contribute to the pathogenesis of liver fibrosis by regulating the activation states of HSCs⁶. In the steady state, Kupffer cells (KCs), a major macrophage subset in liver, reside along the length of liver sinusoid where they contribute to the clearance of toxic materials from bloodstream. One of the core features of MASH is the massive accumulation of blood-borne monocytes⁷ that further differentiate into monocyte-derived macrophages, replacing the resident KCs^8,9,10,11. Known as scar-associated macrophages (SAMs)^12,13, these recruited monocyte-derived cells best resemble lipid-associated macrophages (LAMs) that aggregate to surround dead adipocytes in obese adipose tissue¹⁴, and are therefore named hepatic LAMs (hLAMs)⁸. There are reports that SAMs crosstalk with various nonparenchymal cells, thereby contributing to the progression of MASH to liver fibrosis in both mouse and human^13,15. However, the impact of monocyte-derived macrophages on the progression to liver fibrosis is still poorly understood because the inhibition of Ccr2-mediated monocyte recruitment can also aggravate the progression to liver fibrosis in mice fed a high-fat diet¹¹. There is an urgent need to identify the regulatory mechanisms controlling monocyte-derived macrophage behavior to understand their function in promoting or suppressing the progression of MASH to liver fibrosis.

To understand the mechanisms driving the differentiation of monocyte-derived macrophages, we identified Egr2, a transcription factor that is enhanced in liver monocytes and macrophages in mice with MASH. Liver fibrosis was ameliorated in mice deficient in Egr2 in immune cells of myeloid lineage. Profibrotic macrophage signatures¹⁶ including Spp1, Fabp5, and Cd63, but not Trem2, were repressed in Egr2-deficient liver macrophages. Single-cell RNA-sequencing (scRNA-seq) analysis showed that the proportion of hLAMs in the monocyte-derived macrophage pool was reduced, and the differentiation of monocytes was biased toward another subset of macrophages expressing high levels of KC signatures that are distinct from either hLAMs or resident KCs owing to the absence of Egr2. In this study to identify niche-specific environmental factors regulating Egr2 expression in liver infiltrating monocytes, we found that long-chain fatty acids upregulate Egr2 in macrophages. Moreover, oral administration of long-chain unsaturated fatty acid decreased the proportion of hLAMs liver macrophages in vivo.

Through this study, we have uncovered the role of Egr2 in the generation of profibrotic macrophages from monocytes. This knowledge may be applied to the development of Egr2-targeted therapy for preventing fibrosis in patients with MASLD.

Results

Egr2 expression is progressively enhanced in liver macrophages during the development of MASH

To analyze the relationship between the development of liver fibrosis and monocyte-derived macrophages, we used a mouse model of choline-deficient amino acid-defined high-fat diet (CDA-HFD)-induced MASH¹⁷ that rapidly progresses to liver fibrosis. First, we analyzed the progression of MASLD in CDA-HFD-fed mice. Microscopically, feeding of CDA-HFD promoted hepatocellular steatosis in 7 weeks (Fig. 1a, left). Compared to HFD which takes 6 months for liver fibrosis to develop¹⁸, liver fibrosis was detectable at week 7 and further progressed within the next 5 weeks by feeding CDA-HFD (Fig. 1a-c). Next, we analyzed the composition of immune cells in CDA-HFD-induced MASH liver. Massive infiltration of Lin (B220, CD90.2, NK1.1, and SiglecF)^-CD11b⁺Ly6G^-Ly6C^hiF4/80^- monocytes into liver was observed in 4 weeks (Fig. 1d, middle and Supplementary Fig. 1a). Lin^-CD11b⁺Ly6C^loF4/80⁺Tim4⁺ resident KCs were rapidly replaced by Lin^-CD11b⁺Ly6C^loF4/80⁺Tim4^- monocyte-derived macrophages in 4 weeks and were barely detectable as early as week 7 (Fig. 1d, middle and right). Immunohistochemistry also demonstrated that Tim4 expression was reduced in macrophages in MASH liver (Fig. 1e). Localization of macrophages was drastically changed in MASH liver, that is, aggregates of F4/80⁺ macrophages are forming crown-like structures^11,19 that may be surrounding dead hepatocytes in MASH liver (Fig. 1e).

**Fig. 1: Egr2 is upregulated in liver-infiltrating monocytes and monocyte-derived macrophages in MASH liver.**

Given that most resident KCs were replaced by Tim4^- monocyte-derived macrophages before the progression to liver fibrosis, we speculated that the phenotype of monocyte-derived macrophages at 7 weeks might be associated with the transition from steatosis to fibrosis. To understand the molecular mechanisms regulating the phenotype of monocyte-derived macrophages in MASH liver, we performed bulk RNA-seq analysis of liver Ly6C^loF4/80⁺ macrophages in normal diet (ND)-fed mice and CDA-HFD-fed wild type (WT) mice. Gene expression of liver macrophages was dramatically changed in mice fed CDA-HFD (Fig. 1f); this was characterized by the upregulation of several markers for classic SAMs such as Trem2, Cd9, and Spp1, and the downregulation of KC markers such as Clec4f and Timd4¹³ (Fig. 1g). We then sought transcription factors that were enhanced in liver macrophages of CDA-HFD-fed mice when compared with ND-fed mice (Fig. 1h). Among such transcription factors, we focused on Egr2 because it was most strongly upregulated in monocytes after infiltration into liver at week 7 (Fig. 1i) and progressively enhanced in macrophages during the fibrotic stage of MASLD (Supplementary Fig. 1b). At week 4 when liver macrophages included both Tim4⁺ resident KCs and Tim4^- monocyte-derived macrophages, Egr2 mRNA was more highly expressed in Tim4⁻ monocyte-derived macrophages than in Tim4⁺ resident KCs (Supplementary Fig. 1c), suggesting that Egr2 may play a role in the differentiation of Tim4^- monocyte-derived macrophages in MASLD. Overall, these findings indicate that Egr2 expression in monocyte-derived macrophages is positively correlated with the progression of MASH to liver fibrosis.

Liver fibrosis is ameliorated in mouse lacking Egr2

To determine the role of Egr2 in monocyte-derived macrophage differentiation in MASH, we crossed Lyz2-Cre mice with Egr2-flox mice²⁰ to generate a strain (hereinafter called Lyz2^Cre/+.Egr2^fl/fl mice) in which monocytes and macrophages, but not lymphoid cells, lack Egr2²¹ (Fig. 2a and Supplementary Fig. 2a). First, we compared the composition of immune cells in the liver of MASH-induced Lyz2^Cre/+.Egr2^fl/fl mice and MASH-induced Egr2^fl/fl littermates by flow cytometry. The percentages of Ly6C^hiF4/80^- monocytes and Ly6C^loF4/80⁺Tim4^- monocyte-derived macrophages in liver were similar between the two genotypes of mice at both 7 and 12 weeks (Fig. 2b and Supplementary Fig. 2b). These results indicate that Egr2 is not required for the recruitment of monocytes. Next, we compared the progression of MASLD between Lyz2^Cre/+.Egr2^fl/fl mice and Egr2^fl/fl littermates. The absence of Egr2 did not change the degree of inflammation or steatosis (Fig. 2c–f and Supplementary Fig. 2c, d). On the other hand, a marked difference in the degree of fibrosis was evident at week 12 (Fig. 2g). As shown in Fig. 2h, the amount of collagen was decreased in the liver of CDA-HFD-fed Lyz2^Cre/+.Egr2^fl/fl mice compared with WT littermates. Amelioration of fibrosis was validated also on the basis of fibrosis-associated gene expression levels in tissue (Fig. 2i). Collectively, these findings indicate that the Egr2 expression by monocytes and macrophages is responsible for the development of liver fibrosis in MASH.

**Fig. 2: Liver fibrosis is ameliorated in *Egr2*-deficient mice.**

Monocyte-derived macrophages fail to acquire profibrotic phenotype in the absence of Egr2

To assess the differences between monocyte-derived macrophages in MASH-induced Egr2^fl/fl mice and Lyz2^Cre/+.Egr2^fl/fl mice, we performed bulk RNA-seq analysis of CD11b⁺ Ly6G^-Ly6C^loF4/80⁺ monocyte-derived macrophages sorted from the liver of Lyz2^Cre/+.Egr2^fl/fl mice and Egr2^fl/fl littermates. Unbiased clustering confirmed that the biological replicates from each group adopted a distinct transcriptional signature (Fig. 3a). Differential gene expression analysis revealed that 520 genes were differentially expressed by at least twofold (123 and 397 genes downregulated and upregulated in Lyz2^Cre/+.Egr2^fl/fl mice compared with Egr2^fl/fl mice, respectively, Fig. 3b, c). We confirmed that known downstream targets of Egr2 (Itgax and Siglecf) were indeed repressed in Lyz2^Cre/+.Egr2^fl/fl mice (Supplementary Fig. 3a). Gene set enrichment analysis (GSEA) showed that the global gene expression of WT monocyte-derived macrophages was related to classic SAM signatures (Fig. 3d, left and 3e, top). On the other hand, the gene expression of Egr2-deficient monocyte-derived macrophages was enriched for KC signatures (Fig. 3d, right and 3e, bottom). Of note was that the expression levels of some key SAM signatures, such as Trem2, were similar between the two genotypes of macrophages (Fig. 3e, top). As the upregulation of KC markers in Egr2-deficient Ly6C^loF4/80⁺ macrophages cannot be explained by the increase of Tim4⁺ resident KCs at 7 weeks (Fig. 2b, also see scRNA-seq data discussed later in Fig. 4a), these findings suggest that Egr2 is required for the full equipment of SAM phenotype by monocyte-derived macrophages, which may promote the transition from simple steatosis to fibrosis over the course of MASLD. Genes upregulated in Egr2-deficient macrophages were enriched for gene ontology (GO) terms associated with vasculature and blood vessel development, suggesting their commitment to tissue regeneration (Fig. 3f).

**Fig. 3: Egr2-deficient macrophages fail to equip SAM characteristics.**

**Fig. 4: scRNA-seq analysis of liver inate immune cells in WT and *Egr2*-deficient mice.**

Heterogeneity of monocyte-derived macrophages in MASH liver

Given that the gene expression of monocyte-derived macrophages was enriched for KC markers in Lyz2^Cre/+.Egr2^fl/fl mice (Fig. 3d, e), although resident Tim4⁺ KCs were almost completely replaced by Tim4^- monocyte-derived macrophages by week 7 (Fig. 2b), the differentiation of monocyte-derived macrophages might be altered by Egr2-deficiency in MASH. Hence, to gain an insight into the global effects of Egr2 deletion on monocyte-derived macrophage differentiation in MASLD liver, we performed scRNA-seq analysis of liver innate immune cells. To this end, we sorted liver CD45⁺Lin (B220, CD90.2, NK1.1, and SiglecF)^-CD11b⁺ cells of an Egr2^fl/fl mouse and a Lyz2^Cre/+.Egr2^fl/fl mouse fed CDA-HFD for 7 weeks and performed sequencing using 10x Chromium platform. Fifteen thousand three hundred and ninety-four cells passed quality control and were clustered using Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction analysis²² within Seurat v4 (Fig. 4a). Cell types were unbiasedly annotated by using SingleR²³ (Supplementary Fig. 4a) or manually annotated on the basis of DEGs (Supplementary Fig. 4b, c). scRNA-seq analysis showed the expansion of neutrophils (Clusters 5, 8, and 16), Ly6C^hi monocytes (Cluster 4), dendritic cells (Clusters 1, 12, and 13), and monocyte-derived macrophages (Clusters 2 and 7) in MASH (Fig. 4a and Supplementary Fig. 4d). Resident KCs (Clusters 3 and 14) accounted for the largest population in the steady state in both Egr2^fl/fl and Lyz2^Cre/+.Egr2^fl/fl mice (Fig. 4a, top right, and Supplementary Fig. 4d). In MASH, on the other hand, KCs were rarely observed in both mouse genotypes (Fig. 4a and Supplementary Fig. 4d). Monocytes and monocyte-derived macrophage clusters that were distinct from resident KCs were dramatically expanded in both mouse genotypes in MASH (Fig. 4a and Supplementary Fig. 4d). These data clearly exclude the possibility that the upregulated expression of KC markers by Egr2-deficient macrophages in bulk RNA-seq analysis (Fig. 3d, e) may be ascribed to the expansion of KCs within the Ly6C^loF4/80⁺ macrophage cluster. The proportions of myeloid cells were similar between the two genotypes of mice in MASH (Fig. 4a and Supplementary Fig. 4d). To precisely analyze the phenotypes of monocytes and monocyte-derived macrophages in MASH, KCs, NK cells, neutrophils, and dendritic cells were excluded, and the remaining cells were re-clustered to yield 11 clusters of monocytes and Tim4^- monocyte-derived macrophages (Fig. 4b). Clusters 1, 4, and 7 represent Ly6C^hi monocytes based on their high expression of Ly6c2 and Ccr2 (Fig. 4b–d). Clusters 5 and 0 represent Ly6C^lo monocytes and Ly6C^int monocytes, respectively (Fig. 4b–d). We found that Tim4^- monocyte-derived macrophages were separated into clusters 3 and 6 in MASH (Fig. 4b–d). Cluster 3 was slightly expanded, whereas cluster 6 was apparently reduced in Lyz2^Cre/+.Egr2^fl/fl mice compared with Egr2^fl/fl mice (Fig. 4b, e).

To understand the relationship between Egr2 expression and the composition of monocyte-derived macrophages, we examined whether clusters 3 and 6 correspond to any one of the monocyte-derived macrophages reported previously. Remmerie’s group separated liver monocyte-derived macrophages into hLAMs (cells that correspond to SAMs) and monocyte-derived KCs (moKCs)⁸, although the functions of each subset were not precisely defined. We calculated the expression of marker genes for hLAMs and moKCs by Tim4^- monocyte-derived macrophage clusters 3 and 6 generated by our scRNA-seq analysis by using AddModuleScore²⁴. This analysis revealed that cluster 6 aligned closely with the gene expression pattern of hLAMs, which was characterized by the increased expression of Spp1, Fabp5, Cd9, and Egr2 (Fig. 4d, f, left and Supplementary Fig. 4e). On the other hand, cluster 3 showed transcriptional similarities to moKCs that are characterized by increased moKC signatures such as Il18bp and Clec4f (Fig. 4d, f, right). Altogether, these results suggest an important role played by Egr2 in controlling the proportions of hLAMs and moKCs subpopulations in the monocyte-derived macrophage pool.

Egr2 drives the differentiation of monocytes into hLAMs in MASH liver

On the basis of these findings, we speculated that Egr2 favored the differentiation of liver-infiltrating monocytes into hLAMs, whereas the differentiation pathway was shifted toward moKCs in the absence of Egr2. To infer the mechanism underlying the alteration of differentiation pathways of Egr2^fl/fl and Lyz2^Cre/+.Egr2^fl/fl monocyte-derived macrophages, we performed trajectory analysis by using RNA velocity (scVelo)^25,26. This analysis suggested that Ly6C^int monocytes differentiated mainly into cluster 6 in Egr2^fl/fl mouse (Fig. 5a, top left). On the other hand, in Lyz2^Cre/+.Egr2^fl/fl mouse, the differentiation pathways from monocytes and cluster 6 were directed toward cluster 3 (Fig. 5a, top right), suggesting the role of Egr2 in driving the differentiation of monocytes into hLAMs and in inhibiting the differentiation of hLAMs into moKCs (Fig. 5a, bottom). To validate this computational prediction in vivo, BM monocytes from ND-fed CD45.2⁺ Lyz2^Cre/+.Egr2^fl/fl mouse or Egr2^fl/fl littermate were transferred into CDA-HFD-fed CD45.1⁺ recipients (Fig. 5b, top). The kinetics of the transferred monocytes in the recipient liver was monitored by flow cytometry. This analysis showed that both Lyz2^Cre/+.Egr2^fl/fl and Egr2^fl/fl monocytes differentiated into Ly6C^loF4/80⁺ monocyte-derived macrophages in 3 days (Fig. 5b, bottom). We found that Egr2^fl/fl monocyte-derived Ly6C^loF4/80⁺ macrophages expressed hLAM-associated genes such as Spp1 and Fabp5 in high levels compared with Lyz2^Cre/+.Egr2^fl/fl monocyte-derived ones (Fig. 5c). Collectively, these results validated the computer prediction that Egr2 is required for the full maturation of hLAMs.

**Fig. 5: Egr2 favors the differentiation of liver-infiltrating monocytes into hLAMs.**

Egr2 promotes the differentiation of monocytes into lipid-rich proinflammatory macrophages

To analyze the characteristics of different clusters (clusters 3 and 6) of monocyte-derived macrophages, we sought surface markers that can discriminate the two subsets by flow cytometry. Among the DEGs (Supplementary Data 1) that were upregulated in cluster 6, we found that a combination of CD11c and Mincle was able to separate Tim4^-CD11b⁺Ly6C^loF4/80⁺ monocyte-derived macrophages into CD11c^hiMincle^hi and CD11c^loMincle^lo subfractions (Fig. 6a). The percentage of CD11c^hiMincle^hi macrophages in Ly6C^hiF4/80⁺ macrophages was positively correlated with the progression of MASH (Supplementary Fig. 5a). To confirm whether CD11c^hiMincle^hi Ly6C^loF4/80⁺ cells and CD11c^loMincle^lo Ly6C^loF4/80⁺ cells correspond to monocyte-derived macrophage clusters defined by scRNA-seq analysis, we sorted Tim4^-Ly6C^loF4/80⁺CD11c^hiMincle^hi and Tim4^-Ly6C^loF4/80⁺CD11c^loMincle^lo cells, examined their gene expression by RNA-seq analysis, and compared the relative expression of signature genes discriminating clusters 3 and 6 defined by scRNA-seq and bulk RNA-seq data. Bulk RNA-seq analysis of Tim4^-Ly6C^loF4/80⁺CD11c^hiMincle^hi cells yielded expression profiles concordant to those of cluster 6 defined by scRNA-seq analysis when marker genes were compared, whereas bulk RNA-seq analysis of Tim4^-Ly6C^loF4/80⁺CD11c^loMincle^lo cells gave expression profiles that were aligned with those of cluster 3 (Fig. 6b). GSEA showed that CD11c^hiMincle^hi macrophages yielded a gene expression pattern concordant to that of hLAMs (Fig. 6c, top). On the other hand, the gene expression pattern of CD11c^loMincle^lo macrophages aligned well with that of moKCs (Fig. 6c, bottom). The differential expression of several hLAM markers (upregulated in CD11c^hiMincle^hi macrophages) and moKC markers (upregulated in CD11c^loMincle^lo macrophages) was verified by qPCR (Supplementary Fig. 5b). By performing scRNA-seq analysis, we were able to show the reduction of cluster 6 and the expansion of cluster 3 in Lyz2^Cre/+.Egr2^fl/fl mice with MASH (Fig. 4b, e). Consistent with the scRNA-seq data, CD11c and Mincle expression was decreased in Lyz2^Cre/+.Egr2^fl/fl Tim4⁻Ly6C^loF4/80⁺ monocyte-derived macrophages (Fig. 6d). Furthermore, the percentage of CD11c^hiMincle^hi fraction of monocyte-derived macrophages was decreased, whereas that of CD11c^loMincle^lo fraction of monocyte-derived macrophages was inversely expanded in Lyz2^Cre/+.Egr2^fl/fl mice (Fig. 6e).

**Fig. 6: CD11c^hiMincle^hi hLAMs are pro-inflamatory.**

Microscopically, the majority of CD11c^hiMincle^hi macrophages corresponding to cluster 6 had abundant pale cytoplasm that was filled with clear vacuoles (“Foamy”, Fig. 6f, top and right). On the other hand, CD11c^loMincle^lo macrophages corresponding to cluster 3 included a large number of cells that are round in shape with dense nuclei (“Not Foamy”, Fig. 6f, bottom and right). CD11c^hiMincle^hi macrophages contained more neutral lipids than their CD11c^loMincle^lo counterparts, as measured by BODIPY staining (Fig. 6g). An enhanced Cd36, Lpl, and Fabp5 expression may facilitate lipid uptake²⁷ by CD11c^hiMincle^hi macrophages (Fig. 6h). Sorted CD11c^hiMincle^hi macrophages and CD11c^loMincle^lo macrophages were stimulated in vitro with lipopolysaccharide (LPS). This analysis showed that CD11c^loMincle^lo macrophages produced much less TNFα than CD11c^hiMincle^hi macrophages (Fig. 6i), and this may explain why liver fibrosis was alleviated in Lyz2^Cre/+.Egr2^fl/fl mice. Overall, these findings indicate that Egr2 is required for the differentiation of monocytes into profibrotic, pro-inflammatory hLAMs in MASLD.

Saturated fatty acids promote Egr2 expression by infiltrating monocytes

As shown in Fig. 1h, Egr2 expression was significantly upregulated in monocytes only after the infiltration into liver, suggesting that some environmental factors accumulated in MASLD liver induce Egr2 expression by the infiltrating monocytes. Several in vitro studies have defined Egr2 as a signature of IL-4 and/or IL-13-dependent alternative macrophage activation^28,29,30. However, our RNA-seq analysis indicated that IL-4 and IL-13 expression was minimal in MASH liver (Supplementary Data 2). We thus set out to determine the environmental factors that trigger Egr2 expression in the infiltrating monocytes. The dysregulation of lipid metabolism results in the accumulation of several long-chain fatty acids in MASH in both mouse and human^31,32. Therefore, we evaluated the effect of long-chain saturated fatty acids on Egr2 induction. Saturated fatty acids upregulated Egr2 mRNA expression in bone marrow-derived macrophages (BMDMs) in a dose-dependent fashion in 6 h (Fig. 7a, top). On the other hand, long-chain unsaturated fatty acids, oleic acid and palmitoleic acid, did not induce Egr2 expression (Fig. 7a, bottom). Furthermore, addition of unsaturated fatty acids repressed the saturated fatty acid-induced Egr2 upregulation (Fig. 7b). Having found that unsaturated fatty acids antagonize the upregulation of Egr2 by saturated fatty acids, we tested whether unsaturated fatty acids control monocyte differentiation in vivo by administrating oleic acid for 2 weeks to MASH mice. This analysis showed that the percentage of CD11c^hiMincle^hi hLAMs among Tim4^-Ly6C^hiF4/80^lo monocyte-derived macrophages was significantly reduced by oral oleic acid (Fig. 7c). Collectively, these findings suggest that the long-chain saturated fatty acids are one of MASH liver-specific environmental cues that induce Egr2 expression by liver-infiltrating monocytes and that the differentiation into hLAMs can be impeded by oleic acid treatment in MASH.

**Fig. 7: Saturated fatty acids stimulate the expression of *Egr2* in macrophages.**

Discussion

Recent studies have disclosed that monocyte-derived macrophages are more heterogeneous than previously considered, that is, the recruited macrophages include at least two subsets with distinct activation states that resemble KCs thereby called moKCs and hLAMs^8,11,33. In this study, we showed that Egr2 played a central role in driving the differentiation of monocytes into hLAMs in MASH liver. In WT mice, infiltrating monocytes gave rise mainly to hLAMs. In Egr2-deficient mice, on the other hand, we found that the differentiation of liver-infiltrating monocytes was biased toward moKCs. RNA velocity analysis placed moKCs at the downstream of hLAMs in MASH (Fig. 5a). As Egr2 expression level was lower in moKCs than in hLAMs (Supplementary Fig. 4e), the downregulation of Egr2 in hLAMs may be associated with the conversion of hLAMs into moKCs and the subsequent amelioration of liver fibrosis. Our scRNA-seq analysis identified transcriptionally distinct Ly6C^hi monocyte subsets, clusters 1 and 4 (Fig. 4b) in MASH liver. Although the RNA velocity analysis did not predict a differentiation of cluster 4 into macrophages, we cannot exclude the possibility that hLAMs and moKCs arise from different monocyte subsets. We noticed that the reduction of hLAMs was not overtly prominent in Lyz2^Cre/+.Egr2^fl/fl mice compared with Egr2^fl/fl mice. This may be ascribed to the functional redundancy of another Egr family transcription factor Egr1³⁴ that was upregulated in Lyz2^Cre/+.Egr2^fl/fl macrophages when compared to Egr2^fl/fl macrophages (Supplementary Fig. 3b). Previously, KCs were thought to play an important role in the initiation of MASLD^35,36. Recently however, roles played by monocyte-derived macrophages are found to be more predominant in the development of liver fibrosis in MASH^13,37. Overall, we provided evidence that the reduction of hLAMs, a subset of monocyte-derived macrophages, alleviates liver fibrosis in MASH, demonstrating their profibrotic role in vivo. Changes between immune cell types other than monocytes and macrophages, which was not analyzed in detail in this study, may also contribute to the development of liver fibrosis.

By using CD11c and Mincle, we separated the monocyte-derived macrophages into CD11c^hiMincle^hi hLAMs and CD11c^loMincle^lo moKCs. Although we were unable to identify the effector molecule responsible for the promotion of liver fibrosis by CD11c^hiMincle^hi hLAMs, we found that CD11c^hiMincle^hi hLAMs produced larger amount of proinflammatory cytokines than CD11c^loMincle^lo moKCs. CD11c and Mincle are markers for macrophages constituting a unique structure named crown-like structure (CLS) in adipose tissue and liver. The CLS-forming macrophages crosstalk with dead hepatocytes, promoting inflammation and fibrosis from MASH¹⁹. In good agreement with the profibrotic role of CLS-forming macrophages in MASH, we showed that the proportion of CD11c^hiMincle^hi hLAMs was reduced in Egr2-deficient mice, which may explain why fibrosis was ameliorated in the absence of Egr2. In this study, we could not determine exactly how Egr2 control the differentiation of hLAMs. As several transcription factors known to regulate monocyte-derived macrophage differentiation were not largely affected by the absence of Egr2 (Supplementary Fig. 3c), we speculate that Egr2 either directly or indirectly regulate the transcription of hLAM-associated genes without impeding the differentiation of monocytes into macrophages.

Once monocytes arrive at their tissue of destination, they undergo extensive differentiation depending on the environmental cues provided by tissue-specific niches, enabling their development into specialized macrophages that support local tissue function^38,39. Following non-inflammatory KC depletion, Notch ligand expressed by liver sinusoidal endothelial cells (LSECs) rapidly activates poised enhancers to induce KC lineage-determining factor LXRα in monocytes⁴⁰. In intact liver, hepatocyte-derived desmosterol, a cholesterol precursor, and LSEC-derived TGF-β coordinately convert infiltrating monocytes into Kupffer-like cells in 2 weeks. However, environmental factors that initiate the differentiation program of monocyte-derived macrophages in MASLD were not identified. Comprehensive lipidomic analysis of MASH patient livers specified a signature of lipid species discriminating MASH patients from healthy or steatosis patients³². That study revealed the accumulation of long-chain fatty acids as a result of dysregulated FADS1 desaturase activity with 100% specificity and sensitivity in MASH patients. Here, we showed that long-chain saturated fatty acids but not their unsaturated counterparts induced Egr2 expression in monocytes and macrophages. Even more, we found that one of unsaturated fatty acids, oleic acid downregulated Egr2 level in saturated-fatty acid-treated macrophages. How monocytes discriminate saturated or unsaturated fatty acids in the context of Egr2 transcription should be determined. Strikingly, oral administration of oleic acid reduced the proportion of hLAMs in monocyte-derived macrophages in only 2 weeks. Although we demonstrated that the oral administration of oleic acid inhibits the differentiation of monocytes into hLAMs in MASH mice, long-term suppression of liver fibrosis was not examined in this study. It is intriguing whether oral oleic acid therapy can suppress the progression of liver fibrosis from simple steatosis in MASH.

Egr2 is upregulated not only in CDA-HFD-induced MASH model but also in HFD- and AMLN-induced MASH models^9,11. Ramachandran’s group reported that Egr2 level is enhanced in liver macrophages of human MASH patients¹³. These reports suggest that Egr2 plays a key role in promoting the differentiation of monocytes into hLAMs both in mouse and human.

Egr2 is indispensable for maintaining the unique functions and characteristics of alveolar macrophages⁴¹. In lung, Egr2 is induced by local GM-CSF and TGF-β in a PPAR-γ-dependent manner in infiltrating monocytes. These findings suggest that the upregulation of Egr2 driven by a niche-specific factor is a conserved monocyte-derived macrophage response that is present across different tissues. In T cells, Egr2 plays an important role for the maintenance of T cell anergy⁴². LAG3⁺CD4⁺CD25^- regulatory T cells produce a large amount of TGF-β3 in an Egr2-dependent manner, which is responsible for the suppression of systemic humoral immune response in a mouse model of lupus⁴³. Thus, although Egr2 might be a target for liver fibrosis in MASH, Egr2 should be targeted selectively in monocytes and macrophages in order not to inhibit protective responses by LAG3⁺CD4⁺CD25^- regulatory T cells.

Methods

Mice

Six- to twelve-week-old C57BL/6J mice and CD45.1 congenic mice were purchased from CLEA Japan and Sankyo Labo Service, respectively. LyzM-Cre mice (Lyz2-Cre) were provided by RIKEN BRC⁴⁴. Egr2-flox mice were kindly provided by Dr. Charnay²⁰. All mice were bred in specific pathogen-free animal facilities. We have compiled with all relevant ethical regulations for animal use.

Mouse model of MASH

Male mice (6- to 12-week-old) were fed choline-deficient, L-amino acid-defined high-fat diet (CDA-HFD, Research Diets, NJ, U.S.A.) containing 0.1% methionine and 62% kcal fat. In some experiments, mice were given 200 mg/kg oleic acid in 250 μL distilled water or vehicle control by oral gavage 3 times a week for 2 weeks. Serum AST and ALT concentrations were determined enzymatically with a biochemical analyzer, DRI-CHEM NX500sV (Fujifilm).

Purification of spleen lymphocytes

Spleen was squeezed between glass slides, and the cell suspension was filtered through nylon mesh. Red blood cells were lysed with 1x Pharm Lyse (BD Biosciences) for 2 min. Cells were pre-incubated with Fc-blocker (Clone 93, BioLegend) for 5 min on ice and then stained with a mixture of antibodies against surface proteins (detailed in Table 1) for 30 min on ice. Dead cells were excluded by using 7-AAD (Miltenyi Biotech). T cells and B cells were sorted with a FACSAria III (BD Biosciences) cell sorter.

Table 1 Antibodies

Full size table

Isolation of liver leukocytes

Mice were deeply anesthetized with a mixture of medetomidine (0.3 mg/kg weight), midazolam (4 mg/kg weight), and butorphanol (5 mg/kg weight). The liver was perfused with 25 mL of pre-warmed Liver Perfusion Medium (Thermo Fisher Scientific) followed by 25 mL of pre-warmed collagenase perfusion solution (0.5 mg/mL Collagenase IV (Gibco), 136 mM NaCl, 5.4 mM KCl, 5 mM CaCl₂, 0.5 mM NaH₂PO₃, 0.42 mM Na₂HPO₃, 10 mM HEPES, pH 7.5, 5 mM glucose, and 4.2 M NaHCO₃) at a rate of 3 mL/min with a Perista pump (ATTO, Japan) through the portal vein. The liver was carefully removed, minced, and further digested in 25 mL of collagenase perfusion solution for 25 min at 37 °C under gentle rotation. After the enzymatic digestion, cells were passed through a 70-μm cell strainer (BD Biosciences). Hepatocytes were removed by three to five cycles of centrifugation at 100 × g for 2 min. Non-parenchymal cells including leukocytes in the supernatant were centrifuged in 50 mL of serum-free DMEM at 500 × g for 10 min at 4 °C before proceeding to antibody staining for flow cytometry.

In vivo transfer of BM monocytes

BM monocytes were enriched by magnetic sorting with Monocyte Isolation Kit (Miltenyi Biotech) and autoMACSpro separator (Miltenyi Biotech) from naïve CD45.2⁺ Lyz2^Cre/+.Egr2^fl/fl mice or Egr2^fl/fl mice. Those monocytes were transferred intravenously into CD45.1⁺ MASH-induced mice. Differentiation of transferred monocytes in the recipient liver was analyzed 3 days after the injection.

Flow cytometry of liver leukocytes

For flow cytometric measurements, cells were pre-incubated with Fc-blocker (Clone 93, BioLegend) for 5 min on ice and then stained with a mixture of antibodies against surface proteins (detailed in Table 1) for 30 min on ice. Dead cells were excluded by using DAPI (Dojindo) or 7-AAD (Miltenyi Biotech). In some experiments, CD45.2⁺ leukocytes were enriched by magnetic sorting using autoMACSpro (Miltenyi Biotech) before flow cytometry. Cell suspensions were analyzed with a BD FACSCelesta or a FACSAria III (BD Biosciences) cell sorter. Data analysis was performed using FlowJo v10 software (Becton Dickinson and Company).

Quantitative real-time PCR (qPCR)

Total RNA from the sorted cells was extracted with an RNeasy Mini Kit, Micro Kit (QIAGEN, Netherlands), or a FavorPrep Total RNA Extraction Column (Favorgen, Taiwan) according to the manufacturers’ protocols. For qPCR, complementary DNAs (cDNAs) were synthesized by using ReverTra Ace (TOYOBO, Japan). qPCR was performed on cDNA using a THUNDERBIRD SYBR qPCR Mix (Toyobo, Japan). Expression levels were normalized to 18S ribosomal RNA (rRNA). Primer sequences are summarized in Table 2.

Table 2 PCR primer sequences

Full size table

Quantitation of TNFα and IL-6 concentrations

Sorted liver macrophages were stimulated in vitro with 0.1 μg/mL LPS (O55:B5, Sigma) for 24 h. TNFα and IL-6 concentrations in the culture medium were determined by ELISA MAX^TM Standard Set Mouse (BioLegend) according to the manufacturer’s protocols.

Hepatic triglyceride level

Total lipids in the liver were extracted with ice-cold 2:1 (vol/vol) chloroform/methanol. Hepatic triglyceride (TG) concentrations were determined using LabAssay^TM Triglyceride (FUJIFILM Wako, Japan).

Hydroxyproline assay

Hydroxyproline content was measured by using a Hydroxyproline Assay Kit (Sigma) according to the manufacturer’s protocol. In brief, ~100 mg of minced liver was homogenized in a homogenizer (Shakeman 6, BMS, Japan). The homogenate was hydrolyzed with 6 N HCl at 110–120°C for 4 h. After the addition of activated charcoal powder and centrifugation at 20,000 × g for 3 min, 12.5 µL of supernatant was transferred onto a 96-well plate that was placed over a 60 °C-heat block to dry the samples. Fifty microliters of chloramine T/oxidation buffer mixture was added to each well, and incubation was carried out at RT for 5 min. Then, an equal volume of diluted DMAB reagent was added, and incubation was accomplished at 60 °C for 30 min. The absorbance at 560 nm was measured with an iMark^TM Microplate Reader (Bio-Rad, U.S.A.).

Histopathology

Liver tissue was fixed in 10% formaldehyde and embedded in paraffin. 4-μm-thick sections were prepared by a microtome and mounted on silanized glass slides. De-paraffinized sections were stained with either hematoxylin and eosin (H&E) or picrosirius red solution (0.1% Direct Red 80 [Sigma-Aldrich, Missouri, U.S.A.] in saturated aqueous picric acid [Muto chemical, Japan]). The sections were then serially immersed in 70%, 80%, 90%, and 100% ethanol, permeabilized in xylene, and mounted in Marinol (Muto Chemical, Japan). The stained sections were observed under a light microscope (BZ-X700, KEYENCE, Japan). Sirius red positive area was quantitated by using ImageJ Fiji software.

Immunohistochemistry

For the visualization of F4/80 and Tim4, 8-μm-thick fresh frozen sections were immersed in 0.5% H₂O₂/methanol to quench endogenous peroxidase activity. Sections were then blocked with Biotin-Blocking System (DAKO) and then with 0.1 M Tris-HCl, pH 8.0/150 mM NaCl/0.05% Tween x20 (TN blocking buffer). Those sections were incubated with either biotinylated anti-F4/80 (2 μg/mL, BioLegend, Clone CI:A3-1), or rat IgG anti-Tim4 (2 μg/mL, BioLegend, Clone RMT4-54) and biotinylated-anti-rat IgG (×250, proteintech), in TN blocking buffer. Signals were amplified by using TSA Biotin System (AKOYA Biosciences). Biotin-conjugated antigens were detected by Alexa Fluor 750-conjugated streptavidin (Invitrogen). Nuclei were visualized by DAPI. Stained sections were mounted in FluorSave (Millipore) and observed under a fluorescent microscope (BZ-X700, KEYENCE, Japan).

In vitro induction of Egr2 in BMDMs

Femurs and tibias of C57BL/6 mice were used for BM cell collection. BM cells were cultured in DMEM supplemented with 10% FBS/10% CMG14-12 conditioned medium/100 U/mL penicillin-streptomycin (Thermo Fisher Scientific). The medium was replaced on day 3 to remove non-adherent cells. BMDMs were harvested on day 5 or 6, seeded on a 96-w plate, and cultured for another 24 h. BMDMs were stimulated with 0–400 μg/mL of palmitic acid (Cayman), palmitoleic acid (Tokyo Chemical Industry), stearic acid (Fujifilm), or oleic acid (Fujifilm) for 6 h. Then, BMDMs were lysed with Buffer RLT (Qiagen) or FARB buffer (Favorgen) for total RNA extraction.

RNA-sequencing analysis

Total RNA from sorted cells was extracted with either TRIzol LS reagent (Thermo Fisher Scientific), Favorgen RNA Kit (Favorgen), or RNeasy Micro Kit (QIAGEN). cDNA was synthesized and amplified using a SMART-Seq HT Kit (Clontech). RNA-seq libraries were prepared using a Nextera XT Kit (Illumina). Single-end 75-bp sequencing was conducted on a NextSeq 500 platform (Illumina). For low-input RNA-seq analysis of CD11c^hiMincle^hi Ly6C^loF4/80⁺ macrophages and CD11c^loMincle^lo Ly6C^loF4/80⁺ macrophages, cDNA was synthesized and amplified using an NEBNext Poly(A) mRNA Magnetic Isolation Module (New England BioLabs). RNA-seq libraries were prepared using an NEBNext Ultra II Directional RNA Library Prep Kit (New England BioLabs). Paired-end 150-bp sequencing was conducted on an Illumina NovaSeq 6000 platform (Illumina).

Adaptor sequences were removed from raw data using Trimmomatic (ver. 0.36 or 0.39). Sequencing reads after trimming were aligned to a mouse reference genome sequence (GRCm38/mm10) in HISAT2 (ver. 2.2.1). PCR duplicates were removed by using Picard (ver.2.23.9). Read counts per gene were calculated using Cufflinks (ver. 2.1.1) or FeatureCounts (ver. 2.0.1) followed by conversion into transcripts per kilobase million (TPM) based on UCSC mm10. PCA and hierarchical clustering analysis were performed AltAnalyze software⁴⁵. GO enrichment analysis was performed with the PANTHER Classification System⁴⁶. Gene set enrichment analysis was performed using GSEA software (ver. 4.1.0). Standard parameters with gene set permutation type were used for the analysis and differences were considered significant with FDR < 0.25. Gene sets used in this project are listed in Supplementary Data 3.

scRNA-seq analysis

CD45.2⁺Lin (CD90.2, B220, NK1.1, SiglecF)^-CD11b⁺ liver cells from ND-fed or CDA-HFD-fed Egr2^fl/fl mouse or Lyz2^Cre/+.Egr2^fl/fl mouse (one mouse per group) were sorted by FACSAriaIII. Each cell sample was stained with biotin anti-mouse CD45.2 and with different DNA barcode-conjugated PE-streptavidins (BioLegend TotalSeq PE-Streptavidin [A951, A952, A953, and A954]) to identify its origin. Then, the sorted cells were pooled and subjected to scRNA-seq using a BD Rhapsody^TM Single-Cell Analysis System (BD Biosciences), and resultant cDNA was amplified by TAS-Seq protocol as previously described⁴⁷ by ImmunoGeneTeqs Inc. Briefly, on-beads cDNA was poly C tailed under stochastic termination condition by terminal transferase, deoxycytidine, and spiked-in dideoxycytidine. Then, second-strand synthesis and whole-transcriptome amplification were performed by PCR. Size distribution of cDNA and TotalSeq PE-streptavidin libraries was analyzed by a MultiNA system (Shimadzu). The resultant cDNA library was processed into a sequencing library by using an NEBNext Ultra II FS DNA Library Prep Kit for Illumina (New England Biolabs), and sequencing adapters were added to associated TotalSeq PE-streptavidin libraries by PCR. Sequencing was performed by an Illumina NovaSeq 6000 sequencer (Illumina, San Diego, CA, USA) and a NovaSeq 6000 S4 Reagent Kit v1.5 (200 cycles). Pooled library concentration was adjusted to 2.0 nM, and 12% PhiX control library v3 (Illumina) was spiked into the library. The sequencing configuration is as follows: read1 67 base pairs [bp], read2 151 bp, index1 8 bp, and index2 8 bp. Adapter trimming, quality filtering, and mapping to the cell barcode and GRCm38-101 reference transcriptome or TotalSeq PE-streptavidin reference of fastq files were performed by using a previously described pipeline (https://github.com/s-shichino1989/TAS-Seq)⁴⁷. Demultiplexing of scRNA-seq data by TotalSeq PE-streptavidin was performed by using a previously described pipeline (https://github.com/s-shichino1989/TASSeq)⁴⁷.

The resultant gene-expression count matrix was processed for downstream single-cell analyses (integration of four datasets [A951, A952, A953, and A954], UMAP dimension reduction, cell cluster identification, conserved marker identification) using Seurat ver. 4.1.1 in R ver. 4.2.1⁴⁸. Briefly, A951, A952, A953, and A954 datasets were integrated with merge functions of Seurat. Cells that contained a percentage of mitochondrial transcripts >25% were filtered out. PCA analysis was performed against 5054 of highly-variable genes identified by FindVariableFeatures (selection.method = mvp, mean.cutoff = c(0.1, Inf), dispersion.cutoff = c(0.5, Inf)) function in Seurat. 1:57 PCs were selected by Jackstraw analysis and used for clustering analysis. Resolution was set as 1.0 for the FindCluster function. UMAP was performed on significant PCs for visualization in two dimensions. Next, marker genes in each cell cluster were defined by FindAllMarkers function in Seurat (test method = wilcox, minimum expression in each cluster R 20%). In sub-clustering analysis of cluster 0,2,4,6,7,9,10, and 11, PCs were set to 1:95. The resolution of FindCluster function was set to 1.0. Average gene expression in each cluster was calculated by AverageExpression function in Seurat. To assess the characteristics of each macrophage cluster, the module score was calculated with Addmodule score function²⁴ of Seurat package. Gene sets used in this project are listed in Supplementary Data 4.

RNA velocity analysis

For RNA velocity analysis, cDNA reads were mapped to reference genome (build GRCm38_101) as described previously⁴⁹. Then, BAM file was splitted by valid cell barcodes by using nim ver.1.0.6 and hts-nim ver.0.2.23. Resultant BAM files were processed into loom files by using velocyto run with -c and -U options, and the loom files were concatenated by loompy.combine function. RNA velocity estimation of monocyte/macrophages in Egr2^fl/fl mouse or Lyz2^Cre/+.Egr2^fl/fl mouse was performed by using scVelo²⁵ with reticulate package in R ver.4.2.1. Briefly, gene filtering and normalization of Egr2^fl/fl and Lyz2^Cre/+.Egr2^fl/fl data was performed by filter_and_normalize function (min_shared_counts=as.integer (30), min_shared_cells=5), and moments were calculated pp.moments function (n_pcs=35, n_neighbors=30, use_highly_variable=FALSE). Velocity graph was calculated by using tl.velocity function (mode=’dynamical’, use_highly_variable=FALSE) and tl.veelocity_graph function, and visualized by pl.velocity_embedding_stream function (min_mass = 2, size = 15).

Evaluation of lipid uptake by liver macrophages

The morphology of sorted liver CD11c^hiMincle^hi Ly6C^loF4/80⁺ macrophages and CD11c^loMincle^lo Ly6C^loF4/80⁺ macrophages was analyzed by Giemsa staining. In brief, 20,000–25,000 sorted macrophages were cytospun on a glass slide and stained with Diff-Quick (Dade Behring) according to the manufacturer’s protocol. The percentage of cells containing lipid inclusions was determined in high-power fields by two independent scientists (A.I. and K.A.) who were blinded to the identities of the sample. Statistical significance was assessed by the chi-square test. BODIPY staining (Thermo Fisher) was performed after surface antibody staining for 15 min at 37 °C, and analysis was performed by FACSCelesta (BD).

Statistics and reproducibility

Data were analyzed either by analysis of variance (ANOVA) followed by multiple comparison, or by the paired or unpaired t-test with Prism (GraphPad Software) unless otherwise stated. P values < 0.05 were considered significant.

Study approval

All animal experiments were performed in accordance with applicable guidelines and regulations and were approved by the Tokyo University of Pharmacy and Life Sciences (TUPLS) animal use committee (Approval numbers: L19-21, L20-18, L21-18, L21-19, L22-17, L22-18, and L23-08).

Reporting summary

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

Data availability

Bulk RNA-seq and scRNA-seq data generated for this study have been deposited at GEO and are publicly accessible using accession numbers GSE263970, GSE263972, GSE263973, GSE263974, and GSE263975. Additional supplementary files include Supplementary Data 1–4. Source data behind the graphs in the paper can be found in Supplementary Data 5.

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Acknowledgements

This study was supported in part by Grant-in-Aid for Transformative Research Areas (A) (22H05190 to K.A.), Grants-in-Aid for Scientific Research (B) (20H03473 to Masato, T.), Grants-in-Aid for Scientific Research (C) (21K06877 to K.A.), Grants-in-Aid for Challenging Exploratory Research (21K19387 to Masato, T.), Grant-in-Aid for Transformative Research Areas (B) (22H05064 to SS), the Japan Agency for medical Research and Development (AMED)-CREST (JP18gm1210002 to Masato, T. and Minoru, T.), AMED-PRIME (JP21gm6210025 to S.S.), MEXT Promotion of Distinctive Joint Usage/Research Center Support Program at the Advanced Medical Research Center, Yokohama City University (JPMXP0618217493, JPMXP0622717006, and JPMXP0723833149 to K.A.), and ONO Medical Research Foundation (to K.A.). We thank Dr. Kikuchi for generating Lyz2^Cre/+.Egr2^fl/fl mice in the early stage of this project, Ms. Yokoi for secretarial assistance.

Author information

These authors contributed equally: Masato Tanaka, Kenichi Asano.

Authors and Affiliations

Laboratory of Immune Regulation, School of Life Sciences, Tokyo University of Pharmacy and Life Sciences, Tokyo, 192-0392, Japan
Ayaka Iwata, Juri Maruyama, Shibata Natsuki, Masato Tanaka & Kenichi Asano
Department of Immunology, Yokohama City University Graduate School of Medicine, Kanagawa, 236-0004, Japan
Akira Nishiyama & Tomohiko Tamura
Advanced Medical Research Center, Yokohama City University, Kanagawa, 236-0004, Japan
Tomohiko Tamura
Department of Regenerative Medicine, Research Institute National Center for Global Health and Medicine, Tokyo, 162-8655, Japan
Minoru Tanaka
Division of Molecular Regulation of Inflammatory and Immune Diseases, Research Institute for Biomedical Sciences, Tokyo University of Science, Chiba, 278-0022, Japan
Shigeyuki Shichino
Department of Biochemistry, Toho University School of Medicine, Tokyo, 143-8540, Japan
Takao Seki
Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033, Japan
Toshihiko Komai, Tomohisa Okamura & Keishi Fujio

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Ayaka Iwata
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Contributions

AI performed the majority of experiments, with help from J.M., N.S., and K.A., and prepared figures. A.N. and T.T. performed RNA-seq analysis. Minoru, T. proofread the paper. S.S and T.S. performed scRNA-seq analysis and processed the data. T.K., T.O., and K.F. provided Egr2-flox mice and proofread the paper. Masato, T. supervised the project and proofread the paper. KA designed the study and wrote the paper.

Corresponding authors

Correspondence to Masato Tanaka or Kenichi Asano.

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Competing interests

S.S. reports advisory role for ImmunoGeneTeqs, Inc; stock for ImmunoGeneTeqs, Inc. All other authors declare no competing interests.

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Communications Biology thanks Kyoung-Jin Oh, Enis Kostallari and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Joao Valente. A peer review file is available.

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Iwata, A., Maruyama, J., Natsuki, S. et al. Egr2 drives the differentiation of Ly6C^hi monocytes into fibrosis-promoting macrophages in metabolic dysfunction-associated steatohepatitis in mice. Commun Biol 7, 681 (2024). https://doi.org/10.1038/s42003-024-06357-5

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Received: 05 October 2023
Accepted: 20 May 2024
Published: 03 June 2024
DOI: https://doi.org/10.1038/s42003-024-06357-5

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