MAT2A promotes atherosclerotic plaque vulnerability by mediating epigenetic reprogramming of macrophages

Domain: biology
Published: 2025-12-16
Editorial outcome: minor_revision

Abstract
Atherosclerosis is mediated by circulating monocytes and lesional macrophages through chronic inflammation accompanied by metabolic reprogramming. Although methionine metabolism enhances the proinflammatory capacity of monocytes/macrophages, its role in atherosclerosis remains unclear. Here, we use untargeted metabolomics and mass spectrometry to demonstrate that monocyte methionine metabolism is associated with vulnerable plaque (thin-cap fibroatheroma [TCFA]) identified by pancoronary optical coherence tomography. Methionine adenosyltransferase Ⅱ alpha (MAT2A), the key enzyme of methionine metabolism, is highly expressed in atherosclerosis. Further epigenetic profiling of inflammatory and migratory gene promoters reveals MAT2A-mediated enrichment of the transcriptional permissive chromatin mark H3K4me3. Myeloid-specific MAT2A ablation and pharmacological inhibition, or a low-methionine diet, reduce monocyte/macrophage inflammation and migration, thereby attenuating plaque vulnerability. Mechanistically, norepinephrine activates the mTOR-c-MYC axis to upregulate MAT2A expression. The combination of norepinephrine and methionine metabolism is associated with TCFA presence and 5-year clinical prognosis. Consequently, MAT2A-mediated methionine metabolism represents a potential therapeutic target for atherosclerosis.

Introduction
Atherosclerosis is a chronic inflammatory disease in which monocytes/macrophages play a central role. The landmark CANTOS (Canakinumab Antiinflammatory Thrombosis Outcome Study), COLCOT (Colchicine Cardiovascular Outcomes Trial), and LoDoCo2 (Low-Dose Colchicine for Secondary Prevention of Cardiovascular Disease) trials demonstrated the clinical relevance of inflammatory risk and the potential of anti-inflammatory therapies for preventing adverse cardiovascular events. Clinical studies have consistently shown that the presence of the prototypical vulnerable, rupture-prone plaque phenotype, namely thin-cap fibroatheroma (TCFA), often coincides with elevated circulating monocyte levels and intraplaque macrophage infiltration. These findings highlight the pivotal role of monocytes and macrophages as mediators of atherosclerosis progression. Notably, monocytes and macrophages exhibit remarkable diversity and plasticity, allowing them to acquire different functional profiles. When exposed to pro-atherogenic stimuli, circulating monocytes give rise to lesional macrophages that maintain a proinflammatory status through profound metabolic reprogramming. Consequently, therapeutic strategies that focus on modulating monocytes/macrophages metabolic reprogramming represent a promising approach for combating atherosclerosis.

Methionine metabolism, which drives epigenetic reprogramming, provides a dynamic biochemical underpinning for inflammatory macrophages. Within the methionine cycle, homocysteine (Hcy), a metabolite downstream of S-adenosyl-methionine (SAM), has recently been suggested to act as a bystander rather than a causative factor in atherosclerosis. In particular, the enzyme methionine adenosyltransferase Ⅱ alpha (MAT2A) primarily mediates SAM synthesis and directly alters the SAM/S-adenosyl-homocysteine (SAH) ratio, known as the “methylation index”, which is a hallmark of cellular methylation capacity. Dysregulation of methionine metabolism, often mediated by MAT2A, can lead to the accumulation of methylated substrates. This disruption alters the delicate balance of cellular methylation, leading to aberrant inflammatory gene expression. Accumulating evidence indicates that MAT2A regulates macrophage inflammation, reactive oxygen species accumulation during ferroptosis, and SAM-dependent, hypomethylation-driven DNA damage, all of which are core drivers of atherogenesis. However, the potential contribution of methionine metabolism in orchestrating pro-atherogenic monocytes/macrophages by influencing epigenetic regulation in driving atherosclerotic plaque progression and vulnerability remains largely unknown.

In this study, we demonstrate that the activation of methionine metabolism, manifested by high SAM/SAH levels, correlates with atherosclerotic plaque vulnerability in patients. Furthermore, we found that MAT2A drives methionine metabolic reprogramming through H3K4me3 modifications, which are crucial for endowing monocytes/macrophages with inflammatory and migratory capabilities during atherosclerosis development in murine models. Inhibition of MAT2A or dietary methionine restriction attenuated atherosclerosis. Our findings demonstrate a pivotal causal role for methionine metabolism in proinflammatory responses and migration in monocytes/macrophages, and identify MAT2A as a promising therapeutic target for atherosclerotic disease.

Results
MAT2A-mediated methionine metabolism is enhanced in monocytes/macrophages from patients and mice with advanced atherosclerotic plaques
Between January 2017 and December 2017, we enrolled a series of 387 stable patients aged 18 years or older with de novo coronary artery disease and an indication for percutaneous coronary intervention who underwent pancoronary optical coherence tomography (OCT) imaging of all three major epicardial coronary arteries (i.e., left anterior descending coronary artery, left circumflex coronary artery, and right coronary artery). OCT is the most reliable intravascular imaging modality for the detection of TCFA, which is an in vivo equivalent of a vulnerable plaque that places patients at high risk for adverse cardiovascular events. Among these patients, 102 (26.4%) had at least one TCFA in the entire coronary vasculature, whereas the remaining 285 (73.6%) had no evidence of TCFA. Baseline characteristics of patients with and without TCFA are presented in the Supplementary Table 1. Patients with TCFA had more traditional coronary risk factors and higher serum lipid profiles compared with patients without TCFA.
To avoid the influence of confounding variables, we selected 38 patient pairs matched for age, gender, traditional coronary risk factors (i.e., diabetes mellitus, dyslipidaemia, current smoking, hypertension, and chronic kidney disease [CKD]), and concomitant statin therapy. The clinical characteristics of these matched pairs were balanced. The matched cohort was considered to be representative of the main study cohort. A higher prevalence of vulnerable plaque characteristics was observed in TCFA-positive patients compared with TCFA-negative patients.
In the matched cohort, we performed untargeted metabolomic profiling on human monocytes. TCFA-positive patients exhibited a distinct metabolic profile compared to TCFA-negative patients. Specifically, one-carbon metabolites SAH, methionine, and serine were significantly elevated in TCFA-positive patients. Pathway analysis further unveiled that methionine metabolism pathway was significantly enriched in the presence of TCFA. Subsequent targeted mass spectrometry revealed that TCFA-positive patients had altered methionine metabolic profiles (i.e., methionine, SAM, and SAH) compared with their counterparts. The SAM/SAH ratio, which serves as an index of methylation potential that controls methyltransferase-mediated methylation reactions, caught our attention. We then divided the aforementioned 76 patients into two groups according to the monocyte SAM/SAH ratio. Qualitative and quantitative OCT analysis showed that patients with high SAM/SAH levels had a higher prevalence of TCFA, thinner fibrous cap thickness (FCT), larger mean lipid arc, and smaller minimal lumen area. In the multivariate analysis, high SAM/SAH levels emerged as an independent predictor of the presence of TCFA (odds ratio [OR]: 4.422; 95% CI: 1.530-12.781; P=0.0061). Representative images illustrating vulnerable plaque characteristics in patients with high SAM/SAH levels are shown. Similar differences in monocyte methionine metabolism were observed in ApoE-/- mice fed a high-fat diet (HFD).
Methionine has been reported to be transported into cells by members of the solute carrier (SLC) family. We next compared the expression of SLC transcripts in monocytes from TCFA-positive and TCFA-negative individuals but found no significant differences between the groups. Similar results were observed in monocytes from HFD-fed ApoE-/- and control mice. To further clarify the cause of increased monocyte methionine metabolism, we measured the levels of methionine metabolism-related enzymes, including MAT2A, SHMT2, SAHH, GLDC, MTHFR, and MTR in circulating monocytes. Of note, MAT2A mRNA levels demonstrated the highest fold-change in TCFA-positive patients compared with the TCFA-negative patients. Meanwhile, monocytes from ApoE-/- mice fed an HFD showed increased MAT2A expression, with similar promotion observed along the atherosclerosis progression. By analyzing single cell RNA sequencing (scRNA-seq) data from human atherosclerotic carotid artery cells, we visualized the expression of MAT2A in atherosclerotic plaques and confirmed its predominant localization to myeloid cells. Furthermore, MAT2A expression was highest in proinflammatory monocytes/macrophages, particularly those from symptomatic patients. In addition, an increased accumulation of MAT2A was observed in CD11b+ myeloid cells and CD68+ macrophages in carotid plaques from symptomatic patients. Similarly, elevated MAT2A expression in CD68+ macrophages was observed in aortic sinus lesions of ApoE-/- mice with advanced atherosclerotic plaques. Therefore, these findings indicate that the MAT2A-mediated methionine metabolism is enhanced in monocytes/macrophages during atherosclerosis development.

MAT2A in monocytes/macrophages is indispensable for mediating the progression of atherosclerosis
To confirm the functional role of macrophage MAT2A in atherosclerotic plaque progression, we generated homozygous myeloid-specific MAT2A knockout on ApoE-/- mice (MAT2Afl/flLyz2CreApoE-/-) by crossing MAT2Afl/flApoE-/- mice with Lyz2-Cre mice, hereafter referred to as MAT2ACKOApoE-/- mice and fed them an HFD as indicated. Indeed, the monocyte methionine catabolic products were significantly reduced following MAT2A deletion. Additionally, methionine can be recycled through the methionine salvage pathway following the SAM-dependent polyamine biosynthesis. Using 13C-labeled methionine, we examined the effects of MAT2A knockout on the methionine cycle and polyamine levels. We observed that upon MAT2A depletion, methionine was preferentially channeled into methylation processes rather than the polyamine synthesis pathways. After 16 weeks of HFD feeding, the atherosclerotic lesion area in the whole aortas and aortic roots of MAT2ACKOApoE-/- mice was significantly smaller than that of MAT2Afl/flApoE-/- mice. Moreover, atherosclerotic plaques from MAT2ACKOApoE-/- mice exhibited ameliorated signs of plaque vulnerability compared with MAT2Afl/flApoE-/- mice, characterized by smaller plaque area, lower necrotic core, but higher collagen content. A more detailed assessment of aortic composition revealed that MAT2ACKOApoE-/- mice had reduced infiltration of CD68+ macrophages and a significantly lower vulnerability index (an established score of several morphological markers indicating the risk of plaque rupture). No significant differences were observed in body weight or serum lipid profiles (total cholesterol, triglycerides, LDL-C, and HDL-C) between the two genotypes. Furthermore, we administered an adeno-associated virus (AAV8) expressing MAT2A to mice and found that it exacerbated atherosclerosis progression. However, this effect not reversed by folic acid (FA) supplementation, despite effective reduction of Hcy levels. Meanwhile, the weight and lipid profiles were comparable across all groups, consistent with the conclusion that the pro-atherogenic effect of MAT2A is independent of Hcy.
Collectively, these data provide compelling evidence that MAT2A-mediated methionine metabolism in monocytes/macrophages plays a key role in enhancing plaque vulnerability and promoting atherosclerosis development.

MAT2A is required to orchestrate the inflammatory response and migratory capacity of monocytes/macrophages
To further elucidate the underlying mechanism alleviating atherosclerosis mediated by MAT2A deficiency, we used RNA sequencing (RNA-seq) to assess the gene expression profiles in bone marrow-derived macrophages (BMDMs) from MAT2ACKO mice and control MAT2Afl/fl mice. A total of 2561 significantly downregulated differentially expressed genes (DEGs) and 2757 significantly upregulated DEGs were detected in MAT2A-deficient conditions. Enrichment analysis revealed that the downregulated DEGs were significantly enriched in pathways related to inflammation and chemotaxis, suggesting potential involvement upon MAT2A knockdown. Moreover, several proinflammatory and migratory genes were significantly downregulated in the absence of MAT2A. In macrophages isolated from atherosclerotic plaques, we observed a similar downregulation of inflammatory and migratory genes, as well as alterations in relevant pathways in MAT2ACKOApoE-/- group compared with controls, recapitulating the changes identified in BMDMs. Based on these findings, we focused subsequent investigations on the role of MAT2A in regulating the inflammatory and migratory capacities of macrophages during atherosclerosis progression.
Analysis of bone marrow and peripheral blood monocytes revealed a significant reduction in Ly6Chi monocytes in MAT2ACKOApoE-/- mice compared with MAT2Afl/flApoE-/- controls. To determine the effect of MAT2A knockout on myelopoiesis, we quantified the hematopoietic stem cells (HSCs) and progenitor cells bone marrow compartment as well as circulating monocytes. Compared with the controls, the MAT2ACKOApoE-/- mice exhibited a reduction in lineage−Sca-1+c-Kit+ cells (LSKs) hematopoietic progenitor cells. Furthermore, the numbers of myeloid progenitors, including granulocyte-monocyte progenitors (GMP) and common myeloid progenitors (CMP), were significantly reduced in the MAT2ACKOApoE-/- mice. Consistently, peripheral blood monocyte counts were reduced in MAT2ACKOApoE-/- mice, indicating that MAT2A regulates bone marrow stem cell proliferation and myeloid bias, thereby promoting the accumulation of circulating monocytes. Additionally, the proportions of MHC Ⅱ+ and CCR2+ macrophages in aortic plaques were lower in the MAT2ACKOApoE-/- mice. We further observed MAT2A deletion reduced the expression of proinflammatory and chemotactic genes in plaque macrophages. Next, we found that chemotaxis towards CCL2 was attenuated in peripheral blood monocytes from MAT2ACKOApoE-/- mice. Moreover, when we labeled Ly6Chi monocytes with Fluoresbrite YG microspheres. we found significantly fewer recruited into the aortic plaques of MAT2ACKOApoE-/- mice compared with controls. Subsequently, we injected PKH67-labeled BMDMs into HFD-fed ApoE-/- mice recipients and found significantly fewer that PKH67 positive cells of MAT2ACKO origin in aortic plaques compared with those receiving MAT2Afl/fl BMDMs. Overall, MAT2A deletion diminishes the recruitment of monocytes/macrophages to atherosclerotic plaques.
We then stimulated MAT2ACKO and MAT2Afl/fl BMDMs in vitro with lipopolysaccharide (LPS) and interferon-γ (IFN-γ). MAT2A deficiency significantly reduced the proportion of proinflammatory (CD11b+CD86+) macrophages and intracellular methionine metabolism, which was reversed by SAM supplementation, indicating the regulatory role of MAT2A on proinflammatory macrophages depends on SAM production. Similarly, SAM restored the decreased migration of MAT2ACKO BMDMs into the well or wound areas, which was consistent with the effects observed upon MAT2A depletion using RNA interference in RAW264.7 cells. Specifically, MAT2A deficiency resulted in lower expression of proinflammatory and chemotactic genes compared with the control, and SAM supplementation partially rescued this suppression. All these results suggest that MAT2A-mediated methionine metabolism is critical to sustaining the proinflammatory and migratory properties of monocytes/macrophages.

MAT2A drives activation and migration of proinflammatory macrophages by modulating H3K4me3
Methionine metabolism has been reported to regulate histone methylation and confer a defined methylation state in proinflammatory macrophages. According to enrichment analysis of RNA-seq data, the downregulated DEGs were also significantly associated with one-carbon metabolism and histone H3-K4 methylation. Consistently, the epigenetic modifiers that cause H3K4me3 deposition (including Setd1a, Setd1b, Kmt2a, Kmt2b, and Wdr5) were significantly downregulated as core components following MAT2A deletion. In ApoE-/- mice treated with AAV-MAT2A, administration of OICR-9429 (small-molecule inhibitor of MLL-WDR5 complex) attenuated MAT2A-induced H3K4me3 enrichment in inflammatory and migratory genes, thereby alleviating atherosclerosis progression. Thus, targeting H3K4me3 deposition mediated by MAT2A-governed epigenetic modifiers could counteract its pro-atherosclerotic effects. Immunoblot analysis of histone modifications in BMDMs confirmed that MAT2A deficiency, which depleted the intracellular SAM pool, led to a marked decrease in H3K4me3, a more modest reduction in H3K36me3, but no change in H3K27me3. In aortic root plaques, the proportion of macrophage-associated nuclei stained positive for H3K4me3 was similarly reduced in MAT2ACKOApoE-/- mice. To identify MAT2A target genes involved in histone methylation during atherosclerosis progression, we isolated BMDMs from MAT2ACKO and MAT2Afl/fl mice and subjected them to CUT&Tag assay using an anti-H3K4me3 antibody. Principal component analysis revealed clear separation based on binding peaks. We observed differential H3K4me3 binding in MAT2A-deficient conditions, and the data indicated that H3K4me3 were predominantly enriched at the promoter centric. The data also showed that H3K4me3 was enriched within 2kb upstream or downstream of gene transcription start sites. To identify H3K4me3-target genes in MAT2ACKO BMDMs, we integrated the CUT&Tag and RNA-seq data. By overlapping annotated H3K4me3-modified and downregulated DEGs, we found 206 downregulated genes with decreased H3K4me3 modification. Further gene enrichment analysis revealed that the downregulated genes with differential H3K4me3 were involved in inflammatory response including IL-1β production and migratory function. Simultaneously, we observed that H3K4me3 modification at the Il1b promoter was reduced upon MAT2A depletion, which is consistent with previous reports indicating that SAM regulates IL-1β expression. Additionally, among these downregulated genes, Aim2, Ccl2, and Mmp9 exhibited substantial H3K4me3 modification based in genomic snapshots. Next, we observed that the MAT2A deficiency-mediated reduction in H3K4me3 enrichment at the promoter regions of Aim2, Ccl2, and Mmp9 was partially rescued by SAM supplementation. The reduced secretion of the AIM2, CCL2, and MMP9 into the cellular supernatant was also restored by SAM supplementation. Furthermore, serum concentrations of these proteins were lower in MAT2ACKOApoE-/- mice and TCFA-negative patients.
In summary, we identified H3K4me3 target genes (including Aim2, Ccl2, and Mmp9) modulated by methionine metabolism and demonstrated that MAT2A deletion disrupts H3K4me3 signaling, thereby limiting the proinflammatory and migratory capacities of macrophages and impeding atherogenesis.

Inhibition of MAT2A and dietary intervention in methionine metabolism ameliorates atherosclerosis
To further evaluate the therapeutic potential of targeting MAT2A-mediated methionine metabolism in atherosclerosis, we employed the MAT2A inhibitor FIDAS-5 in ApoE-/- mice starting at 10 weeks of HFD for 6 weeks. We found that monocyte methionine metabolism was significantly suppressed by FIDAS-5 treatment. Notably, FIDAS-5 reduced atherosclerotic lesion area in the whole aortas and aortic roots. The plaque area and the proportion of necrotic core were effectively decreased in the aortic roots underlying MAT2A inhibition, whereas the collagen content in their plaques was increased. Furthermore, the CD68+ macrophages content and the plaque vulnerability index were reduced for the FIDAS-5 condition. We observed the frequency of Ly6Chi monocyte subsets was lower in the bone marrow and blood following FIDAS-5 treatment. FIDAS-5 administration also downregulated H3K4me3 levels and reduced transcription of Aim2, Ccl2, and Mmp9 in peripheral blood monocytes, while the differences in body weight and serum lipid levels between the two groups were not statistically significant. As expected, additional supplementation of SAM under conditions of MAT2A inhibition by FIDAS-5 would further exacerbate atherosclerosis, confirming that MAT2A promotes atherosclerosis by regulating SAM availability. Further analysis following HCY supplementation in mice with FIDAS-5 management revealed no significant alterations in monocytes/macrophages inflammatory and migratory function, suggesting that MAT2A inhibition regulates macrophage function independently of Hcy.
As an essential amino acid, methionine is primarily sourced from dietary intake and could be regulated via dietary pattern. To determine the efficacy of dietary methionine intervention against atherosclerosis, ApoE-/- mice were fed either a methionine-restricted diet (MRD) or a control diet following HFD initiation. As anticipated, MRD reduced methionine metabolism in peripheral blood monocytes. The aortic arches showed less plaque burden in mice with MRD. Meanwhile, atherosclerotic lesions across entire aortas and aortic sinuses were diminished by MRD. Subsequent analysis of lesions in the aortic roots revealed a decrease in the atherosclerotic plaque and necrotic core areas, while MRD feeding increased the collagen content. Immunohistochemical staining showed that the MRD attenuated CD68+ macrophage accumulation and decreased plaque vulnerability. Bone marrow and peripheral blood monocytes of the MRD groups showed a tendency toward reduction of differentiation into Ly6Chi monocytes. Moreover, we demonstrated that the H3K4me3 levels and the Aim2, Ccl2, and Mmp9 expression were also reduced in peripheral blood monocytes from mice with MRD. In contrast, MRD did not affect body weight, serum cholesterol, and lipoprotein levels. Interestingly, MRD did not alter the progression of atherosclerosis in MAT2ACKOApoE-/- mice, demonstrating that the atheroprotective effect of dietary methionine restriction is dependent on MAT2A.
Taken together, both pharmacological inhibition of MAT2A and dietary methionine restriction alleviate atherosclerosis by attenuating the migration of proinflammatory monocytes/macrophages.

MAT2A is induced by the norepinephrine-mTOR-c-MYC communication axis in monocytes/macrophages during atherosclerosis progression
Sympathetic activation promotes norepinephrine release in atherosclerosis patients, which is involved in the concomitant state of chronic stress. Norepinephrine-cAMP shaft increases MAT2A activity, which may be accompanied by an increase in SAM production. Of note, we measured serum norepinephrine concentrations and concluded that TCFA-positive patients had significantly elevated levels of norepinephrine. To evaluate whether norepinephrine is associated with the upregulation of MAT2A and MAT2A-mediated methionine metabolism in atherosclerosis, we performed an integrative analysis and confirmed that serum norepinephrine levels correlated with the monocytes SAM/SAH ratio. Moreover, patients with high levels of both the SAM/SAH ratio and norepinephrine exhibited the highest incidence of TCFA, whereas those with low levels of both had the lowest incidence.
Subsequently, we evaluated the role of norepinephrine in regulating MAT2A-mediated methionine metabolism during atherosclerosis. We confirmed that serum norepinephrine levels were predominantly elevated in ApoE-/- mice subjected to prolonged atherosclerosis development. We further administered 6-hydroxydopamine (6-OHDA) to deplete norepinephrine in ApoE-/- mice and found significantly reduced monocyte MAT2A expression and methionine metabolism. In addition, an intervention model in HFD-fed ApoE-/- mice using both AAV-MAT2A and 6-OHDA. 6-OHDA treatment markedly attenuated plaque burden, whereas MAT2A overexpression reversed this effect, with no differences in body weight and lipid profiles across all groups. Furthermore, as anticipated, 6-OHDA administration failed to reduce atherosclerosis progression in MAT2A knockout mice, indicating that the pro-atherosclerotic effects of MAT2A in macrophages is dependent on sympathetic activation. Above all, MAT2A is induced by norepinephrine to promote plaque vulnerability.
The mechanistic target of rapamycin (mTOR), a key regulator of metabolic balance and macrophage fate, is activated by neurotransmitter uptake. By comparing levels of the mTOR in aortas isolated from ApoE-/- mice fed an HFD with or without 6-OHDA treatment, we found that 6-OHDA inhibited phosphorylation of mTOR and S6. To identify mTOR-dependent transcription factors regulating of MAT2A, we screened candidates and found that c-MYC depletion specifically reduced Mat2a expression. Consistently, c-MYC was downregulated by 6-OHDA conditions. In c-MYC-deficient BMDMs, the insulin-induced increase in MAT2A expression driven by physiological mTOR pathway activation was abolished, demonstrating the transcriptional dependence of MAT2A on the mTOR-c-MYC axis. Furthermore, norepinephrine promoted Mat2a expression, and this effect was blocked by mTOR inhibition with rapamycin (RAPA). Moreover, c-MYC depletion reduced MAT2A levels, whereas MAT2A silencing did not induce c-MYC downregulation, demonstrating the transcriptional regulation potential by which c-MYC controls MAT2A expression. CHIP-qPCR further suggested that c-MYC binds to MAT2A, and this interaction was prevented by RAPA treatment. Additionally, intervention targeting norepinephrine receptors via propranolol administration or ADRB2 genetic deficiency consistently reduced MAT2A expression and downstream molecules during atherosclerosis progression. Therefore, MAT2A is transcriptionally activated by norepinephrine-evoked mTOR-c-MYC pathway.
Taken together, our results establish that MAT2A-mediated methionine metabolism is modulated by the norepinephrine-mTOR-c-MYC axis in atherosclerosis.

The combination of SAM/SAH and norepinephrine is associated with vulnerable plaque and long-term clinical outcomes in the validation cohort
Between January 2018 and December 2019, 845 stable patients were eligible for pancoronary OCT imaging and 100 matched patient pairs were selected as the validation cohort based on age, gender, traditional coronary risk factors, and concomitant statin therapy after pairwise comparison. Circulating monocyte methionine metabolism levels were determined by targeted mass spectrometry, and serum norepinephrine levels were measured by ELISA. Patients with TCFA (n=237, 28.0%) had a higher prevalence of coronary risk factors than those without TCFA (n=608, 72.0%). 100 matched patients had similar clinical characteristics and well balanced as shown by the standardized mean difference (SMDs) of the baseline characteristics. Clinical characteristics in the matched cohort were representative of the entire cohort. Patient characteristics of both the discovery and validation cohorts were comparable. TCFA-positive patients exhibited a higher prevalence of vulnerable plaque characteristics.
Consistent with the discovery cohort, SAM and SAH concentrations, as well as SAM/SAH levels, were elevated in TCFA-positive patients compared with TCFA-negative controls. Meanwhile, serum norepinephrine levels were significantly higher in TCFA-positive than in TCFA-negative patients. We then divided the 200 patients into two groups based on the monocyte SAM/SAH levels and, separately, on serum norepinephrine levels. OCT analysis showed that patients with high SAM/SAH levels had a higher prevalence of TCFA and other quantitative vulnerable plaque features. A similar pattern was observed in patients with high versus low serum norepinephrine levels. Furthermore, the SAM/SAH ratio showed a negative linear correlation with the thinnest FCT and a positive correlation with the mean lipid arc in plaques, whereas norepinephrine levels showed weaker but significant correlations with these vulnerable characteristics. Linear correlation analysis revealed a positive correlation between the monocyte SAM/SAH ratio and serum norepinephrine levels per patient. After adjustment for other confounding risk factors, both high SAM/SAH levels (OR: 3.779; 95% CI: 1.975–7.228; P<0.0001) and high norepinephrine levels (OR: 2.572; 95% CI: 1.397–4.732; P=0.0024) were independently associated with TCFA presence. The combination thereof performed significantly better than the SAM/SAH (delta AUC 0.052, 95% CI 0.006–0.098, P=0.027) or the norepinephrine alone (delta AUC 0.069, 95% CI 0.026–0.113, P=0.002) for predicting TCFA. Patients were followed for up to 5 years (median of 5.0 years [IQR: 4.2–5.0 years]), and major adverse cardiovascular events (MACE) were the composite of cardiac death, nonfatal myocardial infarction, and unplanned coronary revascularization. Multivariate Cox proportional hazards regression analysis showed that high SAM/SAH levels was independently associated with MACE (HR: 2.395; 95% CI: 1.157–4.956; P=0.0186), whereas high norepinephrine levels increased the odds of MACE, although this was not statistically significant. The Kaplan-Meier curves showed that individuals with high SAM/SAH and norepinephrine levels had the highest incidence of MACE. Collectively, these findings highlight the potential clinical value of combining the SAM/SAH and norepinephrine for predicting the presence of vulnerable plaque and long-term adverse cardiovascular events.

Methods
Study population
Between January 2017 and December 2019, a series of 1232 stable patients aged 18 years or older with de novo coronary artery disease and an indication for percutaneous coronary intervention underwent pancoronary optical coherence tomography (OCT) of all three main epicardial coronary arteries (i.e., left anterior descending coronary artery, left circumflex coronary artery, and right coronary artery) at the 2nd Affiliated Hospital of Harbin Medical University (Harbin, China). The main exclusion criteria were cardiogenic shock, end-stage renal disease, severe liver dysfunction, and contrast media allergy. Patients with left main disease, chronic total occlusion, or extremely tortuous or heavily calcified vessels were excluded due to the anticipated difficulty in performing OCT.
From this population, 387 patients (31.4%) were enrolled between January 2017 and December 2017 as the discovery cohort. A total of 845 participants (68.6%) were included between January 2018 and December 2019 as the validation cohort.
Among the 387 patients included in the discovery cohort, 102 patients presented with at least one thin-cap fibroatheroma (TCFA) in all 3 vessels, while the remaining 285 patients showed no evidence of TCFA. 38 matched pairs of TCFA-positive and TCFA-negative patients, balanced for age, gender, traditional coronary risk factors (i.e., diabetes mellitus, dyslipidaemia, current smoking, hypertension, and chronic kidney disease [CKD]), and concomitant statin therapy were selected. Similarly, from the 845 patients in the validation cohort, 100 matched pairs of patients with or without TCFA were included in the subsequent analysis. In addition, the effect sizes Cohen’ s d (standardized mean difference [SMD]) for pairwise comparisons was reported to evaluate the magnitude of the effect size. The standardized mean differences for sex were 0.07 and 0.02 in the discovery and validation cohorts, suggesting well-balanced matching and minimization of confounding effects due to sex in the two cohorts. Based on this solid statistical evidence, we deliberately utilized both male and female samples in our subsequent mechanistic studies to ensure the generalizability and translational relevance of our findings. This approach ensures that the identified mechanisms are not exclusive to a single sex, thereby strengthening the overall validity and impact of our conclusions for a broader population. Blood samples were collected before the interventional procedures, and isolated monocytes were stored at −80°C. This study was approved by the Ethics Committee of the 2nd Affiliated Hospital of Harbin Medical University (approval No. KY2015-182-04). All subjects provided written informed consent before enrollment. The experiments adhered to the principles of the Declaration of Helsinki.

OCT analysis, and inter- and intra-observer agreement
OCT was performed using a commercially available frequency-domain OCT system (ILUMIEN OPTIS System, Abbott Vascular, Santa Clara, CA). OCT imaging was analyzed according to previously established criteria and consensus using an off-line review workstation (Abbott Vascular). Coronary lesions were classified as lipidic plaques (low signal region with diffuse border) or fibrous plaques (homogeneous and signal-rich region). For each lipidic plaque, the lipid arc was measured at every 1 mm interval throughout the entire lesion; fibrous cap thickness (FCT) was measured 3 times at its thinnest part, and the average value was calculated. Lipid length was recorded on the longitudinal OCT view. The cross-section with minimum lumen area (MLA) was automatically selected by the ILUMIEN console algorithm along the entire length of the assessed coronary segment. Specifically, TCFA was defined as a lipid plaque with the thinnest fibrous cap thickness (FCT) <65 μm and maximum lipid arc >90°.
OCT imaging was analyzed by two blinded expert readers (C.F. and S.L. [Shuang Li]), and a consensus reading was obtained from a third independent investigator (J.D.) when there was discordance between the two readers. Eighty patients were randomly selected to evaluate inter- and intra-observer agreement, as assessed by two independent investigators and by the same investigator at two separate time points with at least a two-week interval, respectively. The inter- and intra-observer agreement for TCFA were 0.86 and 0.92, respectively.

Clinical follow-up
Clinical follow-up was conducted at 1, 3, 6, and 12 months after discharge and then annually by telephone interview and/or clinical visit. Follow-up was censored at 5 years or at last known contact, whichever came first. Major adverse cardiovascular events (MACE) comprised cardiac death, nonfatal myocardial infarction, and unplanned coronary revascularization, which were defined according to the Academic Research Consortium guidelines. Cardiac death was defined as death from myocardial infarction, cardiac perforation or pericardial tamponade, arrhythmia or conduction abnormalities, procedural complications, or any death in which a cardiac cause could not be excluded. Nonfatal myocardial infarction was diagnosed by the detection of raise and fall of cardiac biomarkers (preferably troponin) above the 99th percentile of the upper reference limit, together with evidence of myocardial ischemia with at least one of the following criteria: ischemic symptoms; ECG changes indicative of new ischemia (new ST-T changes or new left bundle branch block); development of pathological Q waves in the ECG; and imaging evidence of new loss of viable myocardium or new regional wall motion abnormalities. Unplanned coronary revascularization was defined as any clinically driven (i.e., unstable angina or progressive angina) repeat percutaneous coronary intervention or surgical coronary artery bypass grafting, which initially was not planned after the index angiography and enrollment in the study.

Human atherosclerotic lesion analysis
Carotid atherosclerotic plaques were obtained from patients undergoing carotid endarterectomy at the 2nd Affiliated Hospital of Harbin Medical University. The baseline characteristics of patients with asymptomatic and symptomatic carotid atherosclerotic disease were shown (Supplementary Table 5). No samples were excluded from the analysis. Investigators were blinded to the patients’ clinical or experimental data. This study was performed in accordance with the Declaration of Helsinki and participants provided written informed consent.

Animal and atherosclerosis induction
All animal procedures were performed in accordance with the Principles of Animal Care established by the National Society for Medical Research and the Guidelines for the Care and Use of Laboratory Animals (NIH publication). All animal experiments were conducted in strict accordance with the guidelines of the Animal Care and Use Committee and were approved by the Institutional Animal Ethics Committee (approval No. YJSDW2023-039). All mice, both female and male, were 8 weeks of age at the initiation of experiments. We used sex-matched mice for each individual experiment. ApoE-/- mice (Beijing Vital River Laboratory Animal Technology) were fed a high-fat diet (HFD) containing 42.09% kcal fat, 19.19% kcal protein, and 38.72% kcal carbohydrate from Junke Biotechnology (JKHFD101, Nanjing, China) for 16 weeks, while control mice were fed a chow diet containing 15.8% kcal fat, 20.3% protein, and 63.9% kcal carbohydrate (JKHFD202, Nanjing, China). The mice had unrestricted access to rodent feed and water. All mice were housed under specific pathogen-free (SPF) conditions with a maintained temperature of 23±3°C, relative humidity of 55% to 60%, and a 12-h light/dark cycle.

Generation of myeloid-specific MAT2A knockout mice
MAT2Afl/fl homozygous and Lyz2-Cre mice were established by Beijing Vital River Laboratory Animal Technology (Beijing, China). Lyz2-Cre mice were crossed with MAT2Afl/fl mice to generate MAT2Afl/fl Lyz2-Cre mice. MAT2Afl/fl Lyz2-Cre ApoE-/- were generated by mating MAT2Afl/fl Lyz2-Cre with ApoE-/- mice (Beijing Vital River Laboratory Animal Technology). 8-week-old MAT2Afl/fl Lyz2-Cre ApoE-/- (MAT2ACKOApoE-/-) mice and control MAT2Afl/fl ApoE-/- mice were fed an HFD for 16 weeks.

Preparations of solutions for in vivo experiments
To establish a pharmacological MAT2A inhibition model, ApoE-/- mice were administered the MAT2A inhibitor FIDAS-5 (10 mg/kg, HY-136144, MedChemExpress, New Jersey, USA) by intragastric gavage. Treatment was initiated after 10 weeks of HFD feeding and continued every other day for 6 weeks.
ApoE-/- mice were fed a methionine-restricted diet (MRD) (0.16% methionine, 0.00% cysteine) purchased from Junke Biotechnology (JK11051301Bi, Nanjing, China) for 6 weeks.
For supplementation with folic acid (FA), animals were randomly assigned to an HFD or an HFD supplemented with FA (20 μg/mL, Sigma-Aldrich, St. Louis, MO, USA) in the drinking water for 16 weeks.
ApoE-/- mice were received standard or DL-Hcy (1.8 g/L, 6038-19-3, Sigma-Aldrich, St. Louis, MO, USA) -containing drinking water for 4 weeks to establish the Hcy supplementation model.
To inhibit H3K4me3 deposition, ApoE-/- mice were administrated intraperitoneally with OICR-9429 (2.5 mg/kg, HY-16993, MedChemExpress, New Jersey, USA) once per 3 days for 8 weeks.
For SAM supplementation, mice were injected intraperitoneally with 2.5 mg/kg SAM (S5109, Selleck, USA), which was dissolved in sterile PBS daily for 5 weeks.
The beta-blocker propranolol was administered in drinking water at a concentration of 0.5 g/L. Based on average daily water consumption of 2.5 mL, this regimen resulted in an estimated daily propranolol dose of 35-40 mg/kg (HY-B0573B, MedChemExpress, New Jersey, USA).
For AAV-PCSK9DY administration, the virus was delivered via a single tail-vein injection at a dose of 1×1011 vector genomes/mouse. From day 1 after injection, the mice were fed either a chow diet or an HFD for 12 weeks.
ADRB2+/− mice were purchased from Cyagen Biosciences, Inc., and intercrossed to generate homozygous ADRB2⁻/⁻ mice.

Chemical ablation of sympathetic nervous fibers
For sympathetic nervous fibers ablation, ApoE-/- mice received intraperitoneal injections of 6-hydroxydopamine (6-OHDA, 250 mg/kg, H4381, Sigma-Aldrich, St. Louis, MO, USA) once weekly for 8 weeks. Peripheral blood monocytes and aortas were collected at the end of the 16-week experimental period. Throughout the experiment, all mice were maintained on an HFD.

AAV construction and injection
AAV-MAT2A was produced by JTSBIO (Wuhan, China). The plasmids were separately co-transfected with the pAAV2/8 trans-plasmid carrying the AAV rep and cap genes and the pAAV helper plasmid into HEK293T cells to generate the AAV8 adenoviruses. AAV-GFP was used as a negative control. Viral titers were measured via PCR using vector-specific primers. For AAV-MAT2A administration, the virus was delivered via a single tail-vein injection at a dose of 1×1011 vector genomes (vg)/mouse during the last 8 weeks of the 16-week HFD feeding.

Cell lines and primary cell culture
The RAW264.7 (TIB-71) and THP-1 (TIB-202) cell lines were obtained from the American Type Culture Collection (ATCC, Manassas, Commonwealth of Virginia, USA). RAW264.7 cells were cultured in complete DMEM medium (C11995500BT, Gibco, Thermo Fisher Scientific, Waltham, MA, USA) containing 10% FBS (FND500, ExCell Bio, Suzhou, China). THP-1 cells were cultured in RPMI 1640 medium (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) containing 10% FBS (FND500, ExCell Bio, Suzhou, China) and incubated with 10 ng/mL PMA (P1585, Sigma-Aldrich, St. Louis, MO, USA) for 48 h. All cell lines were authenticated by examination of morphological and growth characteristics and confirmed to be mycoplasma-free.

Cell transfection
The RAW264.7 and THP-1 cells were seeded in 6-well plates to adhere and transfected 12 h later with Lipofectamine 2000 (11668-019, Thermo Fisher Scientific, Waltham, MA, USA) in accordance with the manufacturer’s directions. Opti-MEM serum-free medium (Gibco, Grand Island, NY, USA) was used to dissolve the transfection mixtures. Using a synthesizer and the AUGC sequence, sense and antisense RNAs were synthesized, annealed into dsRNA, and then purified with HPLC and RPC. The following siRNA duplexes were used: Mouse MAT2A siRNA: GGATACAATTTACCACCTA (Ribo, Guangzhou, China); mouse c-MYC siRNA: CCGUACAGCCCUAUUUCAUTT AUGAAAUAGGGCU GUACGGTT (GenePharma, Shanghai, China); mouse HIF1a siRNA: GAUGUGACAUCCACAGACGAA UUCGUCUUGGGAUGUCACUAG (GenePharma, Shanghai, China); mouse PPARγ siRNA: GGCCUCCCU GAUGAAUAAATT UUUAUUCAUCAGGGAGGCCTT (GenePharma, Shanghai, China); mouse SREBP1 siRNA: GAGCAGAGAUGGCU CUAAUTT AUUAGAGCCAUCUCUGCUCT (GenePharma, Shanghai, China); human MAT2A siRNA: GGAUCGAGGUGCU GUGCUUTT AAG CACAGCACCUCGAUCCTT (GenePharma, Shanghai, China). The medium was replaced with conditioned medium 6 h after transfection. For RNA interference, siRNA was transfected into the cell for 72 h, and the transfection efficiency was detected. Cells were maintained until the following experiments.

Preparations of solutions for in vitro experiments
For the indicated experiments, cells were stimulated with 50 ng/mL Interferon-γ (IFN-γ) (315-05, PeproTech, Cranbury, NJ, USA) and 100 ng/mL lipopolysaccharide (LPS) (L2630, Sigma-Aldrich, St. Louis, MO, USA) for 6 h prior to analysis.
To interfere with intracellular SAM levels, cells were cultured in complete medium containing freshly reconstituted 200 μM SAM (A7007, Sigma-Aldrich, St. Louis, MO, USA) for 48 h.
To investigate the effect of norepinephrine, cells were treated with 10 μM norepinephrine (HY-137158, MedChemExpress, New Jersey, USA) for 24 h. The compound was dissolved in DMSO (D8370, Solarbio, Beijing, China) and then diluted in culture medium to the final concentration.
To activate the mTOR signaling pathway, BMDMs were stimulated with insulin (100 nM, PB180432, Procell, Wuhan, China) for 15 h. To inhibit mTOR activity, rapamycin (RAPA) (553210, Calbiochem, San Diego, CA) was dissolved in pure DMSO to a concentration of 10 mM. This was further diluted in cell medium to a final concentration of 20 nM as described before. Cells were treated with it for 15 h.

Isolation and culture of BMDMs
Bone marrow cells were isolated using the reported procedures. In brief, bone marrow cells were flushed out from the femurs and tibias of 8-week-old mice, and incubated for 7 days with complete DMEM medium (C11995500BT, Gibco, Thermo Fisher Scientific, Waltham, MA, USA) containing 10% FBS (FND500, ExCell Bio, Suzhou, China), 1% penicillin-streptomycin (C0222, Beyotime, Shanghai, China), and murine macrophage colony-stimulating factor (M-CSF, 10 ng/mL, 315-02-1MG, Gibco, Thermo Fisher Scientific, Waltham, MA, USA) to induce BMDMs differentiation. BMDMs were stimulated with 50 ng/mL IFN-γ (315-05, PeproTech, Cranbury, NJ, USA) and 100 ng/mL LPS (L2630, Sigma-Aldrich, St. Louis, MO, USA) for 6 h.

Monocyte Isolation
Peripheral venous blood was collected from enrolled patients into K2-EDTA-coated tubes (Thermo Fisher Scientific, Waltham, MA, USA) and processed within 1 h. The serum and the monocyte-rich component were obtained by centrifugation (CF7D2, HITACHI, Japan). Monocytes were then isolated from the monocyte-rich component according to the protocol of the human peripheral blood monocyte separation kit (TBD2011H05, Tianjin, China).
Mouse monocytes were isolated from peripheral blood using the TBD Mouse Monocyte Isolation Kit (TBD2011M, Tianjin, China).

Untargeted metabolomics analyses
This study analyzed 76 independent biological replicates (38 from TCFA-positive patients and 38 from TCFA-negative patients). The cell samples were spiked with 1 mL of methanol, and added zirconia beads. The samples were centrifuged at 16000g and 4°C for 15 min. The supernatant was evaporated to dryness under mild nitrogen and reconstituted in 50 μL of 50% acetonitrile, including internal standards, before performing UHPLC-HRMS/MS analysis. Chromatographic separation was performed on a Thermo Fisher Ultimate 3000 UHPLC system with a Waters ACQUITY UPLC BEH Amide column (2.1 mm × 100 mm, 1.7 μm) and an Atlantis Premier BEH Z-HILIC column (2.1 mm × 100 mm, 1.7 μm). Untargeted metabolomics analysis was performed using a Thermo Fisher Q Exactive Hybrid Quadrupole-Orbitrap Mass Spectrometry in Heated Electrospray Ionization Positive and Negative mode, respectively. Spray voltage was set to 3.5 kV. Both Capillary and Aux Gas Temperature were 350°C. Sheath gas flow rate was 40 (Arb). Aux gas flow rate was 10 (Arb). S-Lens RF Level was 50 (Arb). The full scan was operated at a high resolution of 35000 FWHM (m/z=200) at a range of 60-900 m/z (positive) and 70-1000 m/z (negative) with AGC Target setting at 3×106. Simultaneously, the fragment ions information of the top 8 precursors each scan was acquired by Data-dependent acquisition (DDA) with HCD energy at 15, 3, 0 and 45 eV, mass resolution of 17500 FWHM, and AGC Target of 1×105.
The raw data of UHPLC-HRMS/MS were preprocessed by Compound Discoverer (version 3.3, Thermo Fisher) with a metabolomics workflow template, where retention times alignment, compound detection, compound grouping, and structural identification were performed. The following parameters were applied in compound identification: (1) accurate mass to charge ratio (m/z) of parent ion, denoted as MS1; (2) mass spectra of fragment ions, denoted as MS2; and (3) chromatographic retention times (RT). Then, we ascertained the classification of these metabolites by cross-referencing each metabolite with the Human Metabolome Database (HMDB, https://hmdb.ca/). To identify metabolites that exhibited significant alterations between groups, a univariate statistical analysis was conducted on the internal standard-normalized peak intensity data. The metabolomics data analysis was performed with all identified metabolites utilizing MetaboAnalyst R 6.0 (https://www.metaboanalyst.ca/). Quantitative Enrichment Analysis (QEA) was performed to integrate quantitative changes in their abundance. The metabolites were mapped into the KEGG database, and the statistical significance of the changes in a pathway’s activity between two groups was evaluated by global testing. The abundances of the annotated metabolites are provided in Supplementary Data 1.

13C-labeled isotope metabolic flux assay
Cellular metabolic flux experiments were performed based on a published study. Methionine is specifically labeled with 13C (200 μm, HY-NO326S5, MedChemExpress, New Jersey, USA), allowing for the tracking of methyl transfer in biochemical processes. The isolated BMDMs were cultured in a methionine-free medium for 24 h. Then, both groups of BMDMs were supplemented with 13C-labeled methionine and incubated for 12 h. After incubation, the cells were lysed for quantitative analysis of 13C-labeled methionine and its metabolites. UHPLC-MS/MS analysis was used for accurate detection of these metabolites.

Mass spectrometry and metabolite quantification
Human monocytes were isolated from the independent discovery (38 TCFA-positive and 38 TCFA-negative patients, n=76) and validation (100 TCFA-positive and 100 TCFA-negative patients, n=200) cohorts. Cell lysates were prepared by extraction with an ice-cold mixture of methanol and water (3/1, v/v) for 20 min on dry ice and snap-frozen in liquid nitrogen. On the day of analysis, samples were vortexed and then centrifuged at 13,300 × g for 10 min at 4°C. A quality control sample was prepared by mixing aliquots of all samples to form a pooled sample. An aliquot of the 800 μL supernatant was transferred to a glass sampling vial for vacuum-dry at room temperature. A 150 μL mixture of acetonitrile and water (3/1, v/v) was added, vortexed for 30 s, ultrasonicated for 3 min, and stored at −20 °C for 2 h. The extract was centrifuged at 15,620 × g, 4°C for 10 min. Samples were transferred to UPLC-MS/MS analysis using an ACQUITY UPLC system interfaced with a triple-quadrupole mass spectrometer. A Waters ACQUITY UPLC BEH Amide (2.1 × 100 mm, 1.7 μm) was used for analysis. Mass spectrometry was performed on the AB SCIEX API 6500+ Qtrap System, with an electrospray ionization (ESI) source, operating in both positive and negative ion modes. Targeted metabolites were analyzed in Schedule multiple reaction monitoring (SRM) mode. Samples were normalized based on protein concentrations using a BCA protein detection kit (P0012, Beyotime, Shanghai, China). Data acquisitions and further analysis were conducted using Analyst software. SCIEX OS-MQ software was used to quantify the metabolites.

Measurement of aortic root lesion development
To achieve a pain-free state before the experimental endpoint, all mice were placed under deep anesthesia with 1% pentobarbital sodium, with their condition continuously monitored and sacrificed by cervical dislocation. Saline was injected into the left ventricle for in situ perfusion, after which the hearts were embedded in optimal cutting temperature compound (Tissue-Tek, Sakura, Torrance, CA) and stored at −80°C. Heart tissues were then serially sectioned into 6 μm frozen sections starting from the point where the aortic valves appeared and continuing until the valve structure was no longer intact. Isolated aortas were briefly rinsed in 75% ethanol for 1 min and subsequently stained using an Oil red O staining kit (G1261, Solarbio, Beijing, China). Images were captured under consistent microscope, camera, and lighting conditions. The Oil red O-stained areas were quantified using Image J (Media Cybernetics, USA) to calculate the lesion area per mouse aortic root. The percentage of lesion area was calculated by dividing the lesion area by the aortic root total area. The aortic roots of mice were fixed in 4% paraformaldehyde (PFA) (BL539A, Biosharp, Hefei, China) at 4°C for 24 h, dehydrated, and embedded in paraffin for precise sectioning into 4 μm slices.
These paraffin-embedded sections were selectively stained with hematoxylin and eosin (H&E) staining kit (G1120, Solarbio, Beijing, China) to evaluate the plaque area. The necrotic core was defined as the acellular non-fibrotic region exceeding 3000 μm2 at the aortic root. Advanced quantitative analysis was conducted using Image J (Media Cybernetics, USA), and the percentage of necrotic core was determined by dividing it by plaque area. Collagen staining of aortic sinus sections was performed using a Masson’s trichrome staining kit (G1340, Solarbio, Beijing, China). The percentage of collagen content within the plaques was computed.

Immunohistochemistry and vulnerability index analysis
Immunohistochemistry was performed following standardized ABC protocols. In brief, immunohistochemistry staining was performed on 4 µm paraffin sections of the aortic sinus using a cocktail of primary antibodies against αSMA (1:50000, ab240654, Abcam, UK) and CD68 (1:100, ab125212, Abcam, UK). Consecutive sections were incubated with appropriate secondary antibodies (ZSGB-BIO, Beijing, China). The sections were incubated with the DAB substrate (ZLI-9018, ZSGB-BIO, Beijing, China) to visualize the bound peroxidase enzyme and then counterstained with hematoxylin. Images were captured, coded, and analyzed blind under uniform microscope, camera, and lighting conditions.
The vulnerability index was adapted from Shiomi et al. and Hartwig et al. and was calculated as the ratio of the sum of CD68+ and necrotic core areas divided by the sum of αSMA+ and collagen areas. Areas were represented as a percentage of the total plaque area, and only mice with plaques advanced enough to display all parameters were considered in the analysis.

Immunofluorescence
Paraffin-embedded human sections were first dewaxed and then fixed in 4% PFA for 5 min. After rinsing with PBS, the sections were permeabilized by incubation with 0.3% Triton X-100 for 30 min at 37°C, followed by antigen repair. Frozen aortic sinus sections from mice were equilibrated to room temperature (RT) for 10 min and then fixed in pre-cooled acetone (−20°C) for 10 min. After rinsing thrice in distilled water, the sections were permeabilized with 0.3% Triton X-100 (T8200, Solarbio, Beijing, China) for 30 min, followed by blocking with 5% BSA (AR0004, Boster Bio, Wuhan, China) for 30 min. Sections were then incubated overnight at 4°C with primary antibodies against MAT2A (1:200, sc-398917, Santa Cruz Biotechnology, USA), CD11b (1:200, ab128797, Abcam, UK), CD68 (1:100, ab125212, Abcam, UK), and H3K4me3 (1:500, 39060, Active Motif, Shanghai, China). On the subsequent day, sections were incubated with appropriate secondary antibodies (ZSGB-BIO, Beijing, China) and then counterstained with DAPI (C1005, Beyotime, Shanghai, China). Finally, images were acquired using a confocal laser scanning microscope (ZElSS LSM700).

Adoptive transfer of macrophages
100 μL of solution containing 106 PKH67-labeled MAT2ACKO and MAT2Afl/fl BMDMs (green, PKH67GL, Sigma-Aldrich, St. Louis, MO, USA) was injected into ApoE-/- mice with HFD for 16 weeks. After 72 h, aortas were collected for further analysis.

Monocyte labeling in vivo
Circulating Ly6Chi monocytes were labeled in vivo by retro-orbital injection of 1 μm Fluoresbrite YG microspheres (Polysciences Inc., PA, USA) diluted 1:4 in sterile PBS 18 h after tail vein injection of 250 μL clodronate loaded liposomes. The efficiency of fluorescent beads labeling was verified 24 h after injection by flow cytometry of monocytes. After 72 h, the number of labeled beads in aortic root cross-sections was quantified using fluorescence microscopy.

Flow cytometry
Mice were euthanized, and fresh tissues were collected. Primary mouse bone marrow cells were isolated by flushing the tibiae and femurs with ice-cold PBS. Peripheral blood was collected, and erythrocytes were lysed with red blood cell lysis buffer (NH4CL2009, TBD, Tianjin, China). The aortas (including the aortic arch, thoracic and abdominal regions) were digested in an enzyme solution containing 250 U/mL Collagenase I (BS163-100MG Biosharp, Hefei, China) at 37°C with constant agitation, and were intermittently disaggregated with a pipette until digestion had completed for 40 min. Then, the digested aortas were gently passed through a 70-μm cell strainer to obtain a single-cell suspension.
Single-cell suspensions from the aorta, bone marrow, and peripheral blood were stained with Fixable Live/Dead stain (423105, BioLegend, San Diego, CA, USA) for 20 min at RT. The harvested cells were washed with cold PBS and subsequently resuspended at a concentration of 1×106 cells per milliliter. Cells were resuspended in 100 μL of PBS containing antibodies and incubated for 30 min at 4°C in the dark, after which they were washed twice. Flow cytometry data were acquired using a FACSCanto II flow cytometer (BD Biosciences, CA, US) and analyzed with FlowJo software (TreeStar Ashland, OR, USA). The antibodies are listed in Supplementary Table 12.
Single-cell suspensions were prepared from aortas. Macrophages were then sorted using a FACSAria III flow cytometer (BD Biosciences, CA, USA). All populations were routinely back-gated to verify gating and purity. F4/80+ macrophages were sorted for subsequent RNA-sequencing (RNA-seq).

RNA extraction, quantitative RT-PCR, and RNA-seq
Total RNA was extracted using TRIzol reagent (15596-026, Thermo Fisher Scientific, Waltham, MA, USA) and then reverse-transcribed using a Transcriptor First Strand cDNA Synthesis Kit (FSQ-301, TOYOBO LIFE SCIENCE, Shanghai, China). qRT-PCR was performed with a FastStart Universal SYBR Green Master Mix (Q511-02, Vazyme Biotech, Nanjing, China). The data were normalized to the mRNA expression of the control gene encoding β-actin or GAPDH in each sample. The sequences of the primers are listed in Supplementary Table 13.
For RNA-seq, BMDMs and plaque macrophages were isolated for total RNA extraction. Samples from three mice of each genotype were sequenced. RNA was isolated using the RNeasy Micro Kit (74004, Qiagen, Hilden, Germany). The cDNA library was constructed and subjected to sequencing. The RNA-seq reads were mapped by the mouse reference genome (GRCm39) from NCBI using htseq (version 2.2.1.0). Raw counts were generated and quantified using htseq-count (version 0.9.1). Differentially expressed analysis was performed using DESeq2 (version 1.20.0) to identify DEGs.

CUT&Tag
BMDMs were collected from MAT2ACKO and control MAT2Afl/fl mice. CUT&Tag assay was performed using the Hyperactive In-Situ ChIP Library Prep Kit for Illumina (TD901-TD902, Vazyme Biotech, Nanjing, China). In brief, prepared concanavalin A-coated magnetic beads (ConA beads) were added to resuspended cells and incubated at RT to bind cells. Cells were then permeabilized with digitonin. Then, the primary H3K4me3 antibody (39060, Active Motif, Shanghai, China), secondary antibody, and the Hyperactive pA-Tn5 Transposase were incubated with the cells that were bound by ConA beads in order. The purified PCR products were evaluated using the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Finally, these libraries were sequenced on the Illumina NovaSeq 6000 platform, and 150 bp paired-end reads were generated. Library sequencing and data analysis were performed. In brief, raw reads were filtered and mapped to the mouse genome GRCm39 using Bowtie2 and subsequently analyzed by the SEACR to detect genomic regions enriched for multiple overlapping DNA peaks that we considered to be putative binding sites. Peaks were then annotated using ChIPseeker to obtain the genes and gene annotations about peaks.

Chromatin immunoprecipitation (ChIP)-qPCR
The ChIP-qPCR was carried out according to the kit protocol (9003S, Cell Signaling Technology, MA, USA). In brief, cells were fixed and lysed, followed by ultrasonication to shear them into appropriate fragments. The obtained chromatin was then incubated overnight with antibodies against H3K4me3 (39060, Active Motif, Shanghai, China), c-MYC (9402S, Cell Signaling Technology, MA, USA), and an isotype-matched control IgG (2729, Cell Signaling Technology, MA, USA). After washing with the wash buffer, the complexes were eluted and purified. Finally, the purified DNA was quantified by qRT-PCR using specific primers. Data were normalized to the corresponding input DNA controls. The sequences of the primers are listed in Supplementary Table 13.

Western blot
Proteins were extracted from atherosclerotic aortas and BMDMs using RIPA lysis buffer (P0013B, Beyotime, Shanghai, China) supplemented with 1 mM PMSF (ST506, Beyotime, Shanghai, China). Protein quantification was performed using a BCA protein detection kit (P0012, Beyotime, Shanghai, China). Samples were then separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) (Beyotime, Shanghai, China) and transferred onto polyvinylidene fluoride (PVDF) membranes (FFP22, Beyotime, Shanghai, China). Subsequently, the membranes were blocked with 5% skimmed milk in 0.1% Tween 20 in TBS, and then incubated overnight at 4°C with primary antibodies in 5% BSA. The following day, membranes were washed with TBST and incubated with the secondary antibodies (ZSGB-BIO, Beijing, China). Protein bands were visualized using enhanced chemiluminescence substrate and imaged with a Tanon 5100 system (Tanon, Shanghai, China). The antibodies used are listed in Supplementary Table 14.

Genotyping
Excised mouse tails were digested in 100 μL of tissue digestion solution (B40013, Selleck Chemicals, Shanghai, China) for 15 min at 55°C, followed by enzyme inactivation at 95°C for 5 min. The digestive lysate was centrifuged at 8580 × g for 5 min, and the supernatant was used as the PCR template (1000 ng). Next, PCR amplification was performed using specific primers, and the products were analyzed by agarose gel electrophoresis. Gels were visualized using a gel imaging system (B40013, Vazyme Biotech, Nanjing, China). The sequences of the genotyping primers are listed in Supplementary Table 13.

Transwell assay
Mice peripheral blood monocytes (mPBMs) were labeled with Vybrant DiO cell-labeling solution (V22886, Invitrogen, Waltham, MA, USA) and resuspended in warmed HBSS at a density of 5 × 106 cells per mL. Millicell cell culture inserts were inserted into each well, and 100 µL of the mPBMs were added to the upper chamber of each insert. Following transmigration, the inserts were removed and the co-cultures containing BMDMs and mPBMs in the lower chambers were fixed with 4% PFA for 20 min. Representative images of the DiO-labeled mPBMs were captured with a brightfield overlay to confirm equivalent BMDMs numbers across treatment groups.
BMDMs and RAW264.7 cells were seeded into the upper chambers of the transwells containing a polycarbonate membrane with 8-μM pores (Corning, NY, USA), and then the chamber was placed in a 24-well plate at 37°C with 5% CO2. Cells were allowed to migrate towards 200 ng/mL of CCL2 (Peprotech, Waltham, MA, USA) in the lower chamber. The non-migrated cells on the upper surface of the membrane were removed using a cotton swab. The migrated cells on the lower surface of the membrane were fixed with 4% PFA, stained with crystal violet staining kit (C0121, Beyotime, Shanghai, China) for 20 min, washed with PBS, and then imaged by light microscopy.

Wound healing
BMDMs and RAW264.7 cells were seeded in 6-well plates and allowed to form a confluent monolayer following stimulation and treatment. Three straight lines were scratched vertically in each well using a pipette tip. The wells were then gently washed with PBS to remove detached cells, and the medium was replaced with fresh DMEM (C11995500BT, Gibco, Thermo Fisher Scientific, Waltham, MA, USA). Images of the scratches were captured immediately (0 h) and 24 h later.

Enzyme-linked immunosorbent assay
Peripheral blood samples from patients and mice were collected and centrifuged at 3810 × g for 10 min to remove sediment. Meanwhile, the cell supernatants were also collected. The levels of inflammatory markers (AIM2, CCL2, and MMP9), norepinephrine, and lipid profiles (total cholesterol, triglyceride, LDL-C, and HDL-C). All measurements were performed using ELISA kits (MeiMian Bio, Shanghai, China).

Human scRNA-seq data
Data from total cells of human atherosclerotic carotid arteries were analyzed in Seurat (version 5.1.0), starting from the author-provided cell count matrix (Gene Expression Omnibus, GSE253903). Quality control involved three steps: retention of genes expressed in at least five cells, exclusion of cells expressing fewer than 200 genes, and elimination of cells expressing over 15% of mitochondrial genes. A total of 10,934 cells were analyzed. Data were normalized using the ‘NormalizeData’ function in Seurat with default parameters. In total, 2,000 highly variable genes were identified using ‘FindVariableFeatures’. Data were scaled using ‘ScaleData’ with default parameters, and principal component analysis was performed using ‘RunPCA’ with default parameters, and clustering was performed at a 0.4 resolution using ‘FindClusters’ (resolution=0.4). Differentially expressed genes in each cluster were identified using the FindAllMarkers function with threshold criteria of FDR <0.05 and |log2(fold change)| >0.25. Cell type annotation was performed based on the expression of known cell lineage markers and on cluster annotations. To analyze myeloid cell subsets, cells corresponding to myeloid cells were extracted and re-clustered using a clustering resolution of 0.6.

Statistics
Categorical variables were expressed as counts and percentages, and differences between groups were compared using the χ2 or Fisher exact test. After assessing data distribution using the Kolmogorov-Smirnov test, continuous variables were expressed as mean ± standard (SD) deviation or median (interquartile range [IQR]) and were compared using the Student’s t-test or the Mann-Whitney U test. Generalized estimating equations methodology was used to take into account the within-subject correlation attributable to multiple plaques analyzed in a single patient. Multivariate logistic regression analysis adjusted for age, gender, traditional coronary risk factors (i.e., diabetes mellitus, dyslipidaemia, current smoking, hypertension, chronic kidney disease [CKD]), and concomitant statin therapy was used to identify whether high SAM/SAH levels or high norepinephrine levels were associated with TCFA. Multivariate Cox proportional hazards regression models adjusted for age, gender, traditional coronary risk factors, and statin at discharge were used to assess the association between high SAM/SAH levels or high norepinephrine levels and the risk of MACE. The correlation between the ratio of SAM/SAH and norepinephrine levels and their correlation with the thinnest fibrous cap thickness (FCT) and the mean lipid arc was tested using Pearson’s correlation analysis. Receiver-operating characteristic (ROC) curve analysis was performed to determine the areas under the curve (AUCs) of SAM/SAH alone, norepinephrine alone, and their combination for discriminating TCFA. Time to-first event Kaplan-Meier curves were estimated to assess the cumulative incidence of MACE during follow-up and were compared using the Log-rank test. Multiple group comparisons were evaluated using analysis of variance (ANOVA). Adjusted P-values were calculated by multiplying raw P-values by g, where g indicates the number of comparisons. Statistical analysis was performed using the GraphPad Prism version 9.0.0 or SPSS 26.0 software (IBM).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Study: Methionine metabolism is important for pro-inflammatory phenotype of macrophages, which propagate\natherosclerosis. However, the role of methionine in atherosclerosis is not known. Methionine adenosyltransferase II alpha\n(MAT2A) is an important enzyme in methionine metabolism and highly expressed in atherosclerosis. MAT2A-deficient\nmacrophages have altered inflammation and migration. Mice with myeloid MAT2A deficiency have reduced plaque\nvulnerability. Epinephrine increases the mTOR-cMyc signaling, enhancing MAT2A expression.\nStrengths:\n1. The significance of MAT2A has been established by both loss and gain of function.\n2. Two different cohorts of patients have been recruited and analyzed.\nWeaknesses:\n1. Fig. 1B: It is not clear why the authors focused on the methionine pathway in their mass spectrometry data. What are the\nother pathways that were altered?\n2. Fig. 3A: RNA seq was performed in BMDM. However, this cel type does not reflect the functions of atherosclerotic\nmacrophages. RNA seq in plaque macrophages in WT and myeloid-specific MAT2A-deficient mice should be performed.\n3. Does MAT2A deficiency decrease macrophage recruitment to atherosclerotic plaques? An adoptive transfer experiment\nwill be useful.\n4. The authors should systemically identify the epigenetic modifiers controlled by MAT2A, causing HeK4me3 deposition and\nvalidate the modifier(s) mechanistically in atherosclerosis.\n5. The uses of beta blockers and genetic deficiency for adrenergic beta receptors will improve the manuscript. Do these\napproaches decrease MAT2A and other downstream molecules along with atherogenesis?\n6. Additionally, the change in MAT2A expression in resident vs. monocyte-derived macrophages should be checked.\n7. Fig. 4J: The H3K4me3 data are missing. This panel is not present.\n8. Fig. 5H: The CD68 staining is not clear. Immunofluorescence microscopy should be performed to count plaque\nmacrophages throughout the manuscript.\n9. How do MAT2A change bone marrow and circulating monocyte numbers? Inflammation or recruitment cannot explain\nthis. Have the authors checked hematopoietic stem and progenitor cells in genetic or pharmacological deficiency of\nMAT2A?\n10. Fig. 6I: The labels could be wrong.\n11. Fig. 1C: The bar graph showing the TCFA data does not have any error bar. Additionally, the Y axis label “Thinnest FCT (um)” may be wrong.", "reviewer_2": "The manuscript by Du and colleagues investigates the role of the enzyme MAT2A in myeloid cells and the impact on\natherosclerosis development. The data are interesting and a relevance of the observations in humans was proposed.\nOverall, the manuscript is well-written.\n1. The gating strategy presented in Figure S4C is unclear. Almost all blood cells appeared to express high levels of CD115\n(CSF1R). This is very surprising since CSF1R expression in mice is restricted to myeloid cells and in particular monocytes\nin the blood circulation. Since this gating strategy was applied to generate the analyses in many figures presented in the\nmanuscript a careful re-analysis of data appears essential to confirm the authors observations.\n2. Atherosclerotic plaques contain diverse immune cell populations and macrophages are the numerically most represented\npopulation. This is also apparent in the scRNA seq data that the authors re-analyzed in Figure 1 H-K. However, in Figure\nS4D, the data presented suggest the only a minor fraction of CD45+ cells are CD68+. This suggests that very few\nmacrophages were extracted or present in the plaque. This discrepancy is unclear for the reviewer. If this is due to a\ntechnical issue, this might also impacted on the analyses generated from the aforementioned data.\n3. While re-analyses of scRNAseq data suggested that MAT2A expression is selective for myeloid cells, the representative\nimage shown in Figure 1L revealed that many MAT2A expressing cells are CD68-. Could the authors comment on this?\n4. The authors observed a lower CCR2 expression on monocytes in Lyz2cre x MAT2Afl/fl x ApoE-/- mice in comparison to\nmonocytes in control mice. However, it remains unclear whether the chemotaxis of blood monocytes towards CCR2 ligands\n(CCL2…) is affected. This should be addressed.\n5. A reduced CCR2 expression could account for diminished monocyte plaque recruitment. Beads-labelling experiments will\nallow to test this hypothesis in vivo and those observation shall strengthen the authors conclusions.\n6. Since CCR2 expression is reduced in monocytes, one could expect also a reduced monocytes egress from bone marrow\nand monocyte accumulation in the latter. Since Lyz2 is also expressed in myeloid-cell progenitor cells, could the authors\nprovide data on whether myelopoiesis is affected by MAT2A deletion?\n7. For the reviewer it remains unclear how MAT2A loss in monocytes and macrophages affected their intracellular\nmetabolism. Flux analysis appear essential to address this question.\n8. The effects of 6-OHDA and CD feeding are interesting but whether monocyte/macrophage MAT2A expression is involved\nremains to be established. The authors should perform similar experiments in Lyz2cre x MAT2Afl/fl x ApoE-/- and control\nmice.", "reviewer_3": "In their manuscript titled “MAT2A promotes atherosclerotic plaque vulnerability by mediating epigenetic reprogramming of\nmacrophages,” Zhuo Du et al. evaluated the role of Met-SAM metabolism mediated by MAT2A in the progression of\natherosclerosis. They obtained substantial data and concluded that MAT2A is highly expressed in monocytes/macrophages,\nand its product SAM contributes to the upregulation of pro-inflammatory/migratory gene expression via H3K4me3, thereby\npromoting plaque vulnerability. Methionine metabolism has been found playing a pivotal role in governing the\nproinflammatory immunological status of macrophages by regulating the epigenetic switch and conferring the metabolic\ndemand. This study adds new evidence to understanding the methionine metabolism in epigenetic regulation in\natherosclerosis. The study is well designed, and the narration is straightforward.\nWhile this represents a novel link between MAT2A and atherosclerosis, the functional mechanisms of the MAT2A-SAM axis\nin vulnerable plaques still need further validated. Many critical questions remain unanswered. Specifically:\n1. Introduction: The literatures provided in the introduction that draws forth the specific scientific question are about\nmethionine metabolism. There are some observations implying the link of MAT2A with atherosclerosis, are more relevant to\njustify the present study.\n2. Specificity of SAM function: Although SAM is well-known as a methyl donor, it also plays an important role in polyamine\nsynthesis. The authors need to determine the specificity of SAM's methylation function and whether SAM can exert its effects\nthrough polyamine synthesis.\n3. Specificity of SAM-H3K4me3 in gene expression for plaque vulnerability: This concern specially goes to Figure 4. When\nH3K4me3, but not H3K36me3, is globally downregulated (A), the authors should also detect the enrichment of H3K36me3 to\nthe promoters of target genes as control. Also, what is the histone modification status of IL-1β promoter since the literature\ndocumented H3K36me3 underpinning IL-1β upregulation in LPS-exposed macrophages. What defines/discriminates the\ndifference of the types of histone methylations, which should be well discussed since this is the down-to-the-bottom\nmechanism for this study.\n4. Independence or synergy with Hcy: Is the mechanism by which MAT2A inhibition or methionine deprivation regulates\nmacrophage function completely independent of Hcy, or is it synergistic with Hcy? Further experiments involving AHCY\nregulation or Hcy supplementation are needed to clarify this.\n5. SAM supplementation in mouse experiments: In Figure 5, after treating mice with MAT2A inhibition or methionine\ndeprivation, it is recommended to supplement SAM at key experimental time points to validate the findings.\n6. Role of mTOR downstream transcription factors: Although the authors demonstrated that the mTOR-c-Myc axis promotes\nMAT2A expression, other classic transcription factors downstream of mTOR, such as HIF1α and PPARγ, should also be\nconsidered. Further experiments are needed to confirm whether mTOR acts through c-Myc.\n7. Statistical figure labeling: In Figure 2B or 2D, there is a lack of labeling for the experimental groups in the statistical charts.\nSimilar issues are present throughout the manuscript and should be corrected.\n8. Reagent descriptions: For reagents mentioned for the first time, such as 6-OHDA, their full names, functions, and\nreferences should be provided. Similar issues should be corrected throughout the manuscript.", "reviewer_4": "The manuscript by Du et al described a role of Methionine adenosyltransferase Ⅱ alpha (MAT2A)in mediating the plaque\nstability through epigenetic regulation of macrophage metabolic reprogramming. Overall, the study is well designed and\nexecuted, and the insights from this study likely to shed light on the molecular mechanism of atherosclerosis. The major\nconcerns is the disconnect of the complexity of methionine metabolism among different cell types within the\nmicroenvironment of atherosclerotic plaques. In particular, the circulating methionine levels may affect other pathways other\nthan the DNA methylations, yet the MAT2A inhibition or dietary manipulation of methionine levels has such a dramatic effect\non atherosclerosis. Below are some minor concerns:\n1. In Fig. 1B, the circulating levels of methionine appears too high comparing to the previous reports.\n2. In Figure 3E, how supplementation of SAM will get into the cell and be responsible for the observed phenotypes.\n3. Fig.4A, quantification of the bands is needed.\n4. In Fig. 7A, how the 100 patients are statistically justified in the validation cohort?\n5. The authors claimed measurements of methionine, SAM, SAH as a metabolomics. It is a routine mass spectrometry\nmethod not a metabolomic study.\n6. In the experiment, why the authors use high fat diets instead of Western diet to feed ApoE KO mice?\nThere are some differences in these two diets in the context of atherosclerosis. In most studies, WD is used rather than high\nfat diet."}, "author_response": "To the Editor and the Reviewers:\nWe would like to thank the editor and reviewers for their professional review,\nconstructive comments and valuable suggestions on our manuscript. Please find our\npoint-by-point response below and the revisions/corrections in the resubmitted files. In\naddition, we have formatted the manuscript according to the journal’s instructions. We\nhope that these changes will strengthen our manuscript and may be sufficient for\npublication.\nReviewer #1 (Remarks to the Author):\nStudy: Methionine metabolism is important for pro-inflammatory phenotype of\nmacrophages, which propagate atherosclerosis. However, the role of methionine in\natherosclerosis is not known. Methionine adenosyltransferase II alpha (MAT2A) is an\nimportant enzyme in methionine metabolism and highly expressed in atherosclerosis.\nMAT2A-deficient macrophages have altered inflammation and migration. Mice with\nmyeloid MAT2A deficiency have reduced plaque vulnerability. Epinephrine increases\nthe mTOR-cMyc signaling, enhancing MAT2A expression.\nStrengths:\n1. The significance of MAT2A has been established by both loss and gain of function.\n2. Two different cohorts of patients have been recruited and analyzed.\nResponse: We would like to thank the reviewer for the positive feedback. All the\ninsightful suggestions raised have been addressed point-by-point in the revised\nmanuscript, significantly strengthening the scientific rigor and overall quality of\nthe work.\nWeaknesses:\n1. Fig. 1B: It is not clear why the authors focused on the methionine pathway in their\nmass spectrometry data. What are the other pathways that were altered?\n1) Response regarding our selection of methionine metabolism for investigation:\nWe appreciate the reviewer's valuable comments. To systematically investigate\nmonocytes amino acid metabolism in atherosclerosis, we selected HILIC-UHPLC-\nThis platform is particularly suited for characterizing polar metabolites like\namino acids. Similar to the findings in target mass spectrometry, the metabolite\nanalysis revealed a significant elevation in methionine metabolism in monocytes\nfrom TCFA-positive patients compared to TCFA-negative patients, with emphasis\non key methionine cycle metabolites including SAH (Supplemental Figure 1). This\nfinding aligns with previous reports suggesting a link between high-protein diets,\naugmented amino acid metabolism (including methionine, an essential amino acid\nwhose systemic levels depend on dietary intake), and atherosclerosis progression\n(Nat Metab. 2024;6(2):359-377; Ageing Res Rev. 2021;72:101500). Furthermore,\nmonocytes/macrophages play a pivotal role in maintaining the inflammatory state\nin atherosclerosis (Immunity. 2017;47(4):621-634), and methionine metabolism has\nbeen implicated in the maintenance of proinflammatory macrophage phenotypes\nthrough metabolic reprogramming (Mol Cell. 2019;75(6):1147-1160).\nConsequently, our decision to focus on methionine metabolism as the core of this\ninvestigation is primarily based on its potential role in connecting dietary protein\nintake, monocyte/macrophage dysfunction, and atherosclerosis.\n2) Response regarding the other altered pathways:\nOur untargeted metabolomics findings demonstrated that plaque vulnerability\nwas typically accompanied by broad metabolic disturbances in monocytes. Among\nthese alterations, methionine metabolism was identified as one of the most\nprominently dysregulated pathways in monocytes from TCFA-positive\npatients. This novel discovery served as the key impetus for our subsequent in-\ndepth investigation. Furthermore, metabolic processes associated with\nglycolysis/gluconeogenesis were also significantly enhanced in TCFA-positive\npatients (Supplemental Figure 1). This enhancement is likely closely linked to the\nheightened activity of methionine metabolism, as a crucial step in the latter\npathway—the MAT2A catalyzed conversion of methionine and ATP to SAM—\nconsumes substantial amounts of ATP generated via glycolysis (Nat Rev Cancer.\n2019;19(11):625-637; Mol Cell. 2019;75(6):1147-1160). Consequently, the\nupregulation of glycolysis/gluconeogenesis may provide essential energetic\nsupport for active methionine metabolism, thereby playing a critical synergistic\nrole in the methionine metabolism-mediated regulation of\nmonocytes/macrophages function during atherosclerosis. The precise mechanisms\nunderlying this synergistic interaction warrant further in-depth validation\nthrough functional experiments in future studies.\nIn addition, in the Results section, following sentences we have added:\n“In the matched cohort, human monocytes underwent untargeted metabolomics\nanalysis. TCFA-positive patients displayed a clearly distinguishable metabolic\nprofile compared to TCFA-negative patients (Figure 1B). Specifically, one-carbon\nmetabolites SAH, methionine, and serine were significantly elevated in TCFA-\npositive patients (Figure 1C, and Supplemental Figure 1A). Pathway analysis\n2\nfurther unveiled that methionine metabolism pathway was significantly enriched\nin the presence of TCFA (Supplemental Figure 1B).” (Page 9; Line 10-16)\nIn the Methods section, the sentences “Untargeted metabolomics:\nChromatographic separation was performed on a ThermoFisher Ultimate 3000\nUHPLC system with a Waters ACQUITY UPLC BEH Amide column (2.1 mm ×\n100 mm, 1.7 μm) and Atlantis Premier BEH Z-HILIC column (2.1 mm × 100 mm,\n1.7 μm). Untargeted metabolomics analysis was performed using a ThermoFisher\nQ Exactive Hybrid Quadrupole-Orbitrap Mass Spectrometry in Heated\nElectrospray Ionization Positive and Negative mode, respectively. Spray voltage\nwas set to 3.5 kV. Both Capillary and Aux Gas Temperature were 350 °C. Sheath\ngas flow rate was 40 (Arb). Auxgas flow rate was 10 (Arb). S-Lens RF Level was\n50 (Arb). The full scan was operated at a high-resolution of 35000 FWHM\n(m/z=200) at a range of 60-900 m/z (positive) and 70-1000 m/z (negative) with AGC\nTarget setting at 3×106. Simultaneously, the fragment ions information of top 8\nprecursors each scan was acquired by Data-dependent acquisition (DDA) with\nHCD energy at 15, 30 and 45 eV, mass resolution of 17500 FWHM, and AGC\nTarget of 1×105.” have been added in the revised manuscript. (Page 45; Line 5-18)\nNew results have been added as Figure 1, B and C, and Supplemental Figure 1, A\nand B\nFigure 1B, The partial least-squares discrimination analysis (PLS-DA) 3D score plots\ndiscriminated TCFA-positive patients and TCFA-negative patients. Figure 1C, The\n3\nvolcano plot of significantly differential metabolites in monocytes of TCFA-positive\npatients vs. TCFA-negative patients. Supplemental Figure 1A, The heatmap shows the\noverlapped differential metabolites based on untargeted metabolomics in monocytes of\nTCFA-positive patients vs. TCFA-negative patients. Supplemental Figure 1B, Bar plot\nshows siginificantly disrupted metabolic pathways.\n2. Fig. 3A: RNA seq was performed in BMDM. However, this cell type does not reflect\nthe functions of atherosclerotic macrophages. RNA seq in plaque macrophages in WT\nand myeloid-specific MAT2A-deficient mice should be performed.\nResponse: According to your suggestion, plaque macrophages from\nMAT2ACKOApoE-/- and control mice were collected for RNA-seq (Supplemental\nFigure 7C). In the subsequent analysis, we found that the results had similar\ndownregulated DEGs related to inflammatory and chemotactic pathways\n(Supplemental Figure 7, D-G), suggesting that myeloid-specific knockout of\nMAT2A plays an important role in regulating macrophage function in\natherosclerotic plaques.\nTherefore, in the Results section, the sentences “In macrophages isolated from\natherosclerotic plaques, we observed downregulation of inflammatory and\nmigratory genes, as well as the pathway alterations in MAT2ACKOApoE-/- group,\nsimilar to the changes identified in BMDMs (Supplemental Figure 7, C-G).” have\nbeen added in the revised manuscript. (Page 14; Line 5-8)\nIn the Methods part, we have added a paragraph to clarify this comparison\nexperiment: “Single-cell suspensions of aortic cells were prepared. Macrophages\nwere then sorted using a FACS Aria III flow cytometer (BD Biosciences, CA, USA).\nAll populations were routinely back-gated to verify gating and purity. F4/80+\nmacrophages were sorted for RNA-sequencing (RNA-seq).” (Page 51; Line 19-20\nand Page 52; Line 1-2)\nNew results have been added as Supplemental Figure 7C-G\nmigratory capacities.\nC, The aortas (n = 6 separated pools of 3 mice each per group) were collected and\ndigested for cell sorting in flow cytometry of plaque macrophages and then analysed\nusing RNA-seq. D and E, Representative inflammatory and chemokine genes in plaque\nmacrophages of MAT2ACKOApoE-/- compared with MAT2Afl/flApoE-/- mice. F, GO\nterm enrichment analysis of downregulated DEGs between two groups. (G) Results of\nthe gene set enrichment analysis (GSEA) based on the gene expression levels of the\nRNA-seq data.\n3. Does MAT2A deficiency decrease macrophage recruitment to atherosclerotic\nplaques? An adoptive transfer experiment will be useful.\nResponse: According to the reviewer’s suggestions, we employed adoptive transfer\nexperiment to investigate the role of myeloid MAT2A in macrophage recruitment\nto atherosclerotic plaques. Specifically, PKH67-labeled BMDMs isolated from\nMAT2ACKO or control mice were injected into ApoE-/- mice fed the HFD for 16\nweeks via the tail vein (Supplemental Figure 10D). Subsequent analysis revealed\nthat PKH67-stained cells from MAT2ACKO donor mice were markedly reduced\nwithin plaque tissues of recipient mice compared to those from control donor mice.\n(Supplemental Figure 10E). These findings collectively indicate that the deficiency\nof MAT2A leads to impaired macrophage recruitment to atherosclerotic plaques.\nIn the Results section, the sentence “Subsequently, we labeled BMDMs with\nPKH67 and injected them into ApoE-/- mice fed with HFD, and noticed that\nwere significantly fewer in ApoE-/- recipients than in those receiving MAT2Afl/fl\nBMDMs (Supplemental Figure 10, D and E). Overall, deletion of MAT2A\ndiminishes the recruitment of monocytes/macrophages to atherosclerotic plaques.”\nhas been added in the revised manuscript. (Page 15; Line 13-18)\nIn the Methods section, the sentences “Adoptive transfer of macrophages: 100 μL\nof solution containing 106 PKH67-labeled MAT2ACKO and MAT2Afl/fl BMDMs\n(green, PKH67GL, Sigma-Aldrich, St. Louis, MO, USA) was injected into ApoE-/-\nmice with HFD for 16 weeks. After 72 h, aortas were collected for further analysis.”\nhave been added in the revised manuscript. (Page 50; Line 8-11)\nNew results have been added as Supplemental Figure 10, D and E:\nSupplemental Figure 10. MAT2A deletion diminishes the monocytes/macrophages\nrecruitment in atherosclerosis.\nD, Schematic of experimental protocol. E, MAT2ACKO BMDMs had lower plaque\ninfiltration than in MAT2Afl/fl BMDMs. Scale bar=50 µm. Unpaired Student’s t-test\nwas used; two-tailed P values are shown, n=5.\n4. The authors should systemically identify the epigenetic modifiers controlled by\nMAT2A, causing H3K4me3 deposition and validate the modifier(s) mechanistically in\natherosclerosis.\nResponse: As the reviewer mentioned, H3K4me3 deposition is facilitated by the\nSET1/MLL complex, which includes several histone lysine methyltransferases\n(e.g., SETD1A, SETD1B, MLL1 [KMT2A] and MLL2 [KMT2B]) and several\nessential protein subunits (e.g., WDR5) (Nat Rev Genet. 2021;22(1):38-58).\nTherefore, we further analysis our RNA-seq data and discovered a series of\n6\ndownregulated core components of the H3K4me3 machinery (including Setd1a,\nSetd1b, Kmt2a, Kmt2b, Wdr5) following MAT2A deletion (Supplemental Table 6).\nAdditionally, we pharmacologically inhibited the MLL-WDR5 complex using the\nselective small molecule antagonist OICR-9429, which represses the H3K4me3\nmark and related gene transcription (J Exp Clin Cancer Res. 2021;40(1):203; Nat\nChem Biol. 2015;11(8):571-578). We observed OICR-9429 partially reversed AAV-\nMAT2A-induced H3K4me3 enrichment in target genes and atherosclerosis\nprogression, suggesting that MAT2A could influence atherosclerosis through the\nextensive regulation of H3K4me3 epigenetic modifiers (Supplemental Figure 11,\nA-C).\nThe sentences “Meanwhile, the epigenetic modifiers that cause H3K4me3\ndeposition (Setd1a, Setd1b, Kmt2a [MLL1], Kmt2b [MLL2], Wdr5) were\nsignificantly downregulated as core components following MAT2A deletion\n(Supplemental Table 6). In AAV-MAT2A-treated ApoE-/- mice, administration of\nOICR-9429 (small-molecule inhibitor of MLL-WDR5 complex [26,27]) attenuated\nMAT2A-induced H3K4me3 enrichment in inflammatory and migratory genes,\nthereby alleviating atherosclerosis progression (Supplemental Figure 11, A-C).\nTherefore, targeting H3K4me3 deposition mediated by MAT2A-governed\nepigenetic modifiers counteracts its pro-atherosclerotic effects.” have been added\nin the revised manuscript. (Page 16; Line 20 and Page 17; Line 1-8)\nIn the Methods part, we have added the following content:\n“To inhibit H3K4me3 deposition, ApoE-/- mice were administrated\nintraperitoneally with OICR-9429 (2.5 mg/kg, HY-16993, MedChemExpres,\nNew\nJersey, USA) once per 3 days for 8 weeks.” (Page 39; Line 19-20 and Page 40; Line\n1)\nThe following new references have been cited in the revised manuscript as well:\n26. Zhang J, et al. Targeting WD repeat domain 5 enhances chemosensitivity and\ninhibits proliferation and programmed death-ligand 1 expression in bladder cancer.\nJ. Exp. Clin. Cancer Res. 40, 203 (2021).\n27. Grebien F, et al. Pharmacological targeting of the Wdr5-MLL interaction in\nC/EBPα N-terminal leukemia. Nat. Chem. Biol. 11, 571-578 (2015).\nNew results have been added as Supplementary Table 6 and Supplemental Figure\nand MAT2ACKO mice, specifically for the genes encoding H3K4me3 epigenetic\nmodification.\nGene Log2 Fold change P-value\nSetd1a -1.30 5.77e-86\nSetd1b -0.76 5.55e-10\nKmt2a [MLL1] -1.04 1.52e-17\nKmt2b [MLL2] -1.78 2.86e-129\nSupplemental Figure 11. The inhibition of H3K4me3 deposition mediated by\nepigenetic modifiers alleviates MAT2A accelerated atherosclerosis.\nA, Schematic representation of the experimental strategy with or without OICR-9429\nand AAV-MAT2A as indicated and subsequent analysis. B, Representative images and\nquantification of H&E staining and Oil Red O staining in aortic roots among AAV-GFP,\nAAV-MAT2A, and AAV-MAT2A+OICR-9429 group mice (n=6). Scale bar=100 µm.\nC, ChIP-qPCR validation of H3K4me3 enrichment on the promoter regions of AIM2,\nCCL2, and MMP9 in BMDMs (n=5). Data are presented as the mean ± SD. One-way\nANOVA with was used; the adjusted P-values are shown.\n5. The uses of beta blockers and genetic deficiency for adrenergic beta receptors will\nimprove the manuscript. Do these approaches decrease MAT2A and other downstream\nmolecules along with atherogenesis?\nResponse: Thanks for the reviewer’s comment. According to your suggestions,\nwe\nhave performed a series of experiments.\nother downstream molecules along with atherogenesis after treating the HFD-fed\nApoE-/- mice with or without propranolol, a beta-adrenergic receptor (β-AR)\nantagonist. Specifically, we discovered that propranolol-treated mice exhibited\nsignificantly reduced atherosclerotic plaque burden compared to controls\n(Supplemental Figure 21, A-C). Further analysis of aortic plaques revealed\nsuppression of the mTOR-c-MYC-MAT2A axis along with propranolol treatment.\nFurthermore, significantly reduced levels of MAT2A-regulated inflammatory and\nchemotactic molecules (AIM2, CCL2, and MMP9) were observed in mice with\npropranolol administration (Supplemental Figure 21, D-F). These findings suggest\nthat beta blockers application participates in delaying atherosclerosis progression\nby modulating the mTOR/c-MYC/MAT2A pathway. Secondly, an atherosclerosis\nmodel was established in adrenergic receptor-deficient (ADRB2-/-) mice via tail\nvein injection of AAV-PCSK9 followed by 12-week HFD feeding. Aortic plaques in\nmice of ADRB2-/- group showed significantly reduced mTOR, c-MYC, and\nMAT2A expression compared to WT controls. Serum analysis revealed decreased\nMAT2A downstream molecular including AIM2 and MMP9 levels with ADRB2\nknockout (Supplemental Figure 21, G-I). These results demonstrate that genetic\ndeficiency of β-AR also regulates downstream factors by modulating the mTOR-\nc-MYC-MAT2A pathway in atherosclerosis. In conclusion, norepinephrine may\nactivate the mTOR–c-MYC pathway via β-AR in macrophages, thereby\nupregulating MAT2A and other downstream molecules to promote atherosclerosis\nprogression.\nIn addition, in the Results section, following sentences have been added:\n“Additionally, intervention experiments on norepinephrine receptors revealed\nthat propranolol administration and ADRB2 genetic deficiency both consistently\nreduced MAT2A expression and the downstream molecules during atherosclerosis\nprogression (Supplemental Figure 21, A-I).” (Page 24; Line 8-11)\nIn the Methods section, these sentences have been added in the revised manuscript:\n“For the beta blocker, propranolol was dissolved in tap water at the concentration\nof 0.5 g/L. On average, mice consumed 2.5mL of water, amounting to an average\ndaily dose of 35-40mg/kg of propranolol (HY-B0573B, MedChemExpres,\nNew\nJersey, USA).” (Page 40; Line 5-7)\n“For AAV-PCSK9DY administration, the virus was delivered via a single tail-vein\ninjection at a dose of 1 × 1011 vector genomes/mouse. From day 1 after injection,\nthe mice were fed either a chow diet or an HFD for 12 weeks.” (Page 40; Line 8-\n10)\n“ADRB2+/− mice were purchased from Cyagen Biosciences, Inc., and interbred to\ngenerate homozygous ADRB2⁻/⁻ generations.” (Page 40; Line 11-12)\nNew results have been added as Supplemental Figure 21\ndownstream molecules along with atherogenesis.\nA, Schematic illustration of the experimental strategy with or without propranolol.\nRepresentative images and quantification of Oil Red O-stained aortic roots (B and C,\nn=5. Scale bar=200 µm). D and E, Immunoblots and quantification of aortic protein\nfrom ApoE-/- mice treated with or without propranolol (n=3). F, ELISA analysis of\nAIM2, CCL2, and MMP9 in serum (n=5). G-I. ADRB2-/- mice fed an HFD for 12\n10\nweeks following tail vein injection of AAV-PCSK9. G and H, Immunoblots and\nquantification of aortic protein from ADRB2-/- and WT mice (n=3). I, ELISA analysis\nin serum from ADRB2-/- and WT mice (n=5). Data are presented as the mean ± SD.\nUnpaired Student’s t-test was used; two-tailed P values are shown.\n6. Additionally, the change in MAT2A expression in resident vs. monocyte-derived\nmacrophages should be checked.\nResponse: In response to the reviewer's suggestion, we re-analyzed the scRNAseq\nof human carotid artery plaques by subdividing intraplaque macrophages into\ntwo subtypes: resident macrophages and monocyte-derived macrophages. Our\nanalysis suggested that MAT2A is highly expressed in monocyte-derived\nmacrophages rather than resident macrophages. Further analysis revealed that\nMAT2A exhibited significantly higher expression levels in monocyte-derived\nmacrophages from symptomatic patients compared to asymptomatic counterparts,\nwhile no significant differences were observed in resident macrophages.\nMAT2A is highly expressed in monocyte-derived macrophages.\nA, UMAP visualization of scRNA-seq profiles of human atherosclerotic carotid artery\nmyeloid cells after subsetting and reclustering (GSE253903). B, Normalized expression\nvalues of MAT2A from human atherosclerotic carotid artery myeloid cells in the\nproportion of resident macrophages and monocyte-derived macrophages. C,\nNormalized expression values of MAT2A from resident macrophages and monocyte-\nderived macrophages of atherosclerosis patients.\n11\n7. Fig. 4J: The H3K4me3 data are missing. This panel is not present.\nResponse: We appreciate the reviewer's careful attention to this inconsistency.\nThis oversight occurred during the revision process for the figure, when the\noutdated text associated with the previous version of the figure on H3K4me3 was\ninadvertently retained. We have revised the manuscript carefully to ensure that\nthe text description is full consistent with the current Figure 4.\n8. Fig. 5H: The CD68 staining is not clear. Immunofluorescence microscopy should be\nperformed to count plaque macrophages throughout the manuscript.\nResponse: In response to the reviewer’s comments, we have supplemented CD68\nimmunofluorescence staining of atherosclerotic plaques and reanalyzed the\nrelevant quantitative data in the corresponding section of the manuscript.\nRepresentative CD68 immunofluorescence images with quantification are shown\nbelow:\nSupplemental Figure 5. MAT2A deletion decreases levels of CD68+ macrophages,\nwhile having no effect on lipid profiles.\nA, Immunofluorescence of CD68+ macrophages in the aortic roots of MAT2Afl/flApoE-\n/- and MAT2ACKOApoE-/- mice (n=10). Scale bar=100 µm.\nSupplemental Figure 13. FIDAS-5 treatment results in the reduction of methionine\nmetabolism, pro-inflammatory monocytes/macrophages and the expression of\nH3K4me3 downstream genes.\nµm.\nSupplemental Figure 16. MRD feeding results in the reduction of methionine\nmetabolism, pro-inflammatory monocytes/macrophages and the expression of\nH3K4me3 downstream genes.\nB, Immunofluorescence of CD68+ macrophages in the aortic roots (n=8). Scale bar=100\nµm.\n9. How do MAT2A change bone marrow and circulating monocyte numbers?\nInflammation or recruitment cannot explain this. Have the authors checked\nhematopoietic stem and progenitor cells in genetic or pharmacological deficiency of\nMAT2A?\nResponse: As the reviewer pointed out, MAT2A may also play a role in regulating\nbone marrow and circulating monocyte numbers. This perspective closely aligns\nwith Comment #6 raised by Reviewer 2. Therefore, based on the reviewer's\nvaluable suggestions, we performed additional flow cytometry analysis of\nmyelopoiesis in MAT2ACKOApoE-/- and MAT2Afl/flApoE-/- mice fed an HFD. The\nresults demonstrated that MAT2A knockout indeed reduced the population of\nhematopoietic stem cells and myeloid progenitor cells in the bone marrow.\nFurthermore, peripheral blood monocyte counts were decreased in\nMAT2ACKOApoE-/- mice compared to controls (Supplemental Figure 8, B-F).\nThese findings indicate that MAT2A regulates bone marrow stem cell\nproliferation and myeloid bias, thereby modulating circulating monocytes levels and contribute\natherosclerosis progression.\nWe have incorporated the following content into the revised Results section of the\nmanuscript:\n“To investigate the effect of MAT2A knockout on myelopoiesis, we examined the\nnumbers of hematopoietic stem cells (HSCs) and circulating monocytes.\nCompared with the control group, the MAT2ACKOApoE-/- group exhibited a\nreduction in the number of hematopoietic progenitor cells, which are defined as\nlineage−Sca-1+c-Kit+ cells (LSKs). Furthermore, the numbers of myeloid\nprogenitors, including granulocyte-monocyte progenitors (GMP) and common\n13\nmyeloid progenitors (CMP), were significantly reduced in the MAT2ACKOApoE-/-\ngroup. Meanwhile, the peripheral blood monocyte quantification was reduced in\nMAT2ACKOApoE-/- mice, indicating that MAT2A regulates bone marrow stem cell\nproliferation and myeloid bias, thereby promoting the accumulation of circulating\nmonocytes (Supplemental Figure 8, B-F).” (Page 14; Line 14-20 and Page 15; Line\n1-4)\nIn the Discussion part, the sentences have been revised as following:\n“After myeloid MAT2A knockout, there was a reduction in myelopoiesis and\nproinflammatory monocytes/macrophages, coupled with diminished migratory\ncapacity. This resulted in a substantial reduction in plaque vulnerability and\ninhibition of plaque progression.” (Page 28; Line 14-17)\nNew results have been added as Supplemental Figure 8, B-F\nSupplemental Figure 8. MAT2A deficiency reduces bone marrow stem cell\nproliferation and myeloid bias.\nB, Schematic of the differentiation path of hematopoietic stem cells toward monocytes\nand neutrophils. C and D, Representative gating strategy and cell count quantifications\nof LSKs (Lin-Sca-1+c-Kit+ cells), GMPs (Lin−Sca-1−c-Kit+CD34+CD16/32+ cells),\nCMPs (Lin−Sca-1−c-Kit+CD34+C16/32- cells) (n=6). E, Flow cytometry plots showing\ngating strategy and quantification for circulating monocytes (CD45+CD11b+CD115+).\nare presented as the mean ± SD. Unpaired Student’s t-test was used; two-tailed P values\nare shown. CMP, common myeloid progenitors; GMP, granulocyte-monocyte\nprogenitors.\n10. Fig. 6I: The labels could be wrong.\nResponse: Thank you for the reviewer’s comment. The mislabeling in the Figure\n6I has been corrected.\nRevised Figure 6I:\nFigure 6. Norepinephrine regulates MAT2A expression in atherosclerosis\nprogression via the mTOR-c-MYC axis.\nI, Occupancy analysis of c-MYC by ChIP-qPCR in BMDMs treated with DMSO\nvehicle or RAPA (20 nM) (n=6).\n11. Fig. 1C: The bar graph showing the TCFA data does not have any error bar.\nAdditionally, the Y axis label “Thinnest FCT (um)” may be wrong.\nResponse: We appreciate the reviewer's engagement with our work and welcome\nthe opportunity to clarify this point. To better illustrate the definition of TCFA-\npositive patient (Figure 1A) and the FCT measurement procedure (Figure 1E), we\nhave included the schematic diagram. With regard to the data presentation in\nFigure 1D, the values labelled as \"TCFA\" represent the percentage distribution of\npatients exhibiting thin-cap fibroatheroma in the three major coronary arteries\nwithin the entire cohort. As these values describe a categorical variable (the\npresence or absence of TCFA in each patient), they are presented as aggregate\npercentages without error bars. For the \"thinnest FCT (µm)\", this parameter was\nquantified at the plaque level. To ensure accurate measurement, three independent\nthickness measurements were obtained at the thinnest region of each plaque, and\nthe final value reflects the mean of these repeated observations.\nNew results have been added as Figure 1, A and E\nwith plaque vulnerability.\nA, The enrollment of TCFA-positive patients defined by OCT and schematic\nrepresentation of the patients screened in the discovery cohort. E, The final FCT was\ndetermined by averaging three measurements (A [yellow], B [green] and C [white])\nperformed at the thinnest part of the fibrous cap using OCT.\nThe manuscript by Du and colleagues investigates the role of the enzyme MAT2A in\nmyeloid cells and the impact on atherosclerosis development. The data are interesting\nand a relevance of the observations in humans was proposed. Overall, the manuscript\nis well-written.\n1. The gating strategy presented in Figure S4C is unclear. Almost all blood cells\nappeared to express high levels of CD115 (CSF1R). This is very surprising since\nCSF1R expression in mice is restricted to myeloid cells and in particular monocytes in\nthe blood circulation. Since this gating strategy was applied to generate the analyses in\nmany figures presented in the manuscript a careful re-analysis of data appears essential\nto confirm the authors observations.\nResponse: We thank the reviewer for highlighting the technical issue with the\ngating strategy for bone marrow cells in the manuscript. To improve the accuracy\nand specificity of the gating strategy, we re-extracted bone marrow cells and\ncirculating monocytes from all groups and performed an additional flow\ncytometry analysis. The numbers of pro-inflammatory monocytes were\nsubsequently re-analyzed based on the new data.\nResults have been revised as Supplemental Figure 8A, Figure 3B, Supplemental\nFigure 13C, and Supplemental Figure 16C:\nSupplemental Figure 8. MAT2A deficiency reduces bone marrow stem cell\nproliferation and myeloid bias.\nA, Gating strategy for monocytes from peripheral blood comparing MAT2ACKOApoE-\n/- with MAT2Afl/flApoE-/- mice fed an HFD.\ncapacities of monocytes/macrophages.\nB, Flow cytometry was performed to quantify CD45+CD11b+CD115+Ly6Chi\nmonocytes from bone marrow or peripheral blood of MAT2ACKOApoE-/- and\nMAT2Afl/flApoE-/- mice fed a 16-week HFD (n=6).\nSupplemental Figure 13. FIDAS-5 treatment results in the reduction of methionine\nmetabolism, pro-inflammatory monocytes/macrophages and the expression of\nH3K4me3 downstream genes.\nC, Representative flow cytometry plots and quantification of CD45+CD11b+CD115+\nLy6Chi monocytes from bone marrow (n=6) and peripheral blood (n=6).\nmetabolism, pro-inflammatory monocytes/macrophages and the expression of\nH3K4me3 downstream genes.\nC, Representative flow cytometry plots and quantification of\nCD45+CD11b+CD115+Ly6Chi monocytes from bone marrow (n=6) and peripheral\nblood (n=6).\n2. Atherosclerotic plaques contain diverse immune cell populations and macrophages\nare the numerically most represented population. This is also apparent in the scRNA\nseq data that the authors re-analyzed in Figure 1 H-K. However, in Figure S4D, the data\npresented suggest the only a minor fraction of CD45+ cells are CD68+. This suggests that very few\nmacrophages were extracted or present in the plaque. This discrepancy is unclear for the reviewer. If this is due to a\ntechnical issue, this might also impacted on the analyses generated from the aforementioned data.\nResponse: We thank the reviewer for their valuable comments, which are intended\nto enhance the rigor of this study. As the reviewer correctly noted, we have\ncarefully re-conducted all the flow cytometry experiments presented in the\nmanuscript on atherosclerotic plaques. Specifically, single-cell suspensions were\nre-isolated from the aortas of MAT2ACKOApoE-/- and MAT2Afl/flApoE-/- mice for\nflow cytometry analysis. Meanwhile, we used a fluorescence minus one (FMO)\ncontrol to eliminate the impact of non-specific background staining and to\nfacilitate accurate gating of CD68-positive and CD68-negative populations. The\nrevised figures illustrate the updated gating strategy and the re-analyzed results\n(Figure 3C and Supplemental Figure 9A). While we observed slight variation in\nthe proportions of MHC Ⅱ and CCR2 on CD45+CD68+ macrophages, the main\nconclusions remain unchanged. These revisions significantly enhance the rigor of\nour flow cytometry data analysis and the credibility of the results.\nNew results have been presented in Supplemental Figure 9A, revised results have\nbeen presented as Figure 3C:\ncapacities in plaques macrophages.\nA, Gating strategy and fluorescence minus one (FMO) control for macrophages from\naortic plaques comparing MAT2ACKOApoE-/- with MAT2Afl/flApoE-/- mice fed an HFD.\nFigure 3. Knockdown of MAT2A reduces the inflammatory and migratory\ncapacities of monocytes/macrophages.\nC, Flow cytometry was performed to quantify MHC Ⅱ and CCR2 on CD45+CD68+\nmacrophages from aortic plaques of MAT2ACKOApoE-/- and MAT2Afl/flApoE-/- mice\nfed a 16-week HFD (n=6).\n3. While re-analyses of scRNAseq data suggested that MAT2A expression is selective\nfor myeloid cells, the representative image shown in Figure 1L revealed that many\nMAT2A expressing cells are CD68-. Could the authors comment on this?\nResponse: We sincerely appreciate the reviewer's meticulous attention. The\ninfiltration of circulating monocytes into plaques and their differentiation into\n20\nmacrophages constitute a pivotal mechanism driving atherosclerosis progression.\nTherefore, we sought to validate the specific role of MAT2A in the\nmonocytes/macrophages by analyzing its expression in CD68+ macrophages.\nHowever, given the highly heterogeneous immune microenvironment within\nplaques—where myeloid populations include monocytes, macrophages, mast cells,\ndendritic cells, and others (Circulation. 2022;145(18):1434-1436)—CD68 staining\nalone fails to comprehensively capture all myeloid subsets. Accordingly, we\nsupplemented our detection with the myeloid-specific marker CD11b. The results\ndemonstrated that the majority of MAT2A-expressing cells were CD11b-positive\nacross both patient groups. While this was notably higher in symptomatic\ncompared to asymptomatic individuals (Figure 1, O and P).\nThe relevant content has been incorporated into the revised Results section of the\nmanuscript:\n“…In addition, an increased accumulation of MAT2A was observed in CD11b+\nmyeloid cells and CD68+ macrophages in carotid plaques from symptomatic\npatients (Figure 1, O and P, Supplemental Figure 2H)...” (Page 11; Line 8-11)\nNew results have been added as Figure 1, O and P\nFigure 1. High levels of MAT2A-mediated methionine metabolism are associated\nwith plaque vulnerability.\nRepresentative immunofluorescence images (O) and quantification (P) of MAT2A and\nCD11b in carotid plaques from symptomatic and asymptomatic patients (n=6. Scale\nbar=50 µm). Data are presented as the mean ± SD and comparisons were made using\nunpaired Student’s t-test; the two-tailed P-values are shown.\n21\n4. The authors observed a lower CCR2 expression on monocytes in Lyz2cre x\nMAT2Afl/fl x ApoE-/- mice in comparison to monocytes in control mice. However, it\nremains unclear whether the chemotaxis of blood monocytes towards CCR2 ligands\n(CCL2…) is affected. This should be addressed.\nResponse: We appreciate the reviewer's insightful comment. We have conducted\nadditional experiments to investigate whether MAT2A regulates CCL2-dependent\nmonocytes chemotaxis. Specifically, peripheral blood monocytes isolated from\nMAT2ACKOApoE-/- and control mice were labeled with Vybrant DiO Cell-Labeling\nSolution and then loaded into the top chambers of Transwell plates. The lower\nchambers contained BMDMs with or without CCL2 stimulation. Our results\ndemonstrated that CCL2 supplementation did not fully rescue the impaired\nmonocyte chemotaxis mediated by MAT2A deficiency. Whereas under conditions\nof sufficient CCL2 stimulation, MAT2A deficiency could not reduce the\nchemotaxis of peripheral blood monocytes (Supplemental Figure 10, A and B).\nThis finding underscores the important regulatory role of MAT2A in CCL2-\nmediated monocyte chemotaxis.\nIn the Results section, the sentence “Next, we found that chemotaxis towards\nCCL2 was attenuated in MAT2ACKOApoE-/- mice (Supplemental Figure 10, A and\nB).” has been added in the revised manuscript. (Page 15; Line 8-9)\nIn the Methods section, the sentences “Mice peripheral blood monocytes (mPBMs)\nwere stained with Vybrant DiO cell-labelling solution (V22886, Invitrogen,\nWaltham, MA, USA) and resuspended in warmed HBSS at a density of 5×106 per\nmL. Millicell cell culture hanging inserts were inserted into each well and 100 µL\nmPBMs were added to the top well of each insert. Post-transmigration, the inserts\nwere removed and co-cultures containing BMDMs and mPBMs in the lower\nchambers were fixed with 4% PFA for 20 min. Representative images of DiO-\nlabeled mPBMs were captured with a brightfield overlay to confirm equivalent\nBMDMs numbers across treatment groups.” have been added in the revised\nmanuscript. (Page 55; Line 15-20 and Page 56; Line 1-2)\nNew results have been added as Supplemental Figure 10, A and B\nSupplemental Figure 10. MAT2A deletion diminishes the monocytes/macrophages\nrecruitment in atherosclerosis.\nA, BMDMs were plated into the bottom chamber of a transwell with or without CCL2\nstimulation (200 ng/mL). Fluorescently labeled MAT2ACKOApoE-/- and\nthe top chamber of the transwell and allowed to pass through a membrane overnight. B,\nTransmigrated mPBMs were visualized and counted per well (n=6). Scale bar=200 µm.\nData are presented as the mean ± SD. Two-way ANOVA was used; the adjusted P-\nvalues are shown, n=6.\n5. A reduced CCR2 expression could account for diminished monocyte plaque\nrecruitment. Beads-labelling experiments will allow to test this hypothesis in vivo and\nthose observation shall strengthen the authors conclusions.\nResponse: To address the reviewer's suggestion, we have performed beads-\nlabelling experiments in the revised manuscript. Specifically, prior to bead\nlabeling, clodronate-loaded liposomes were administered to transiently deplete\ncirculating monocytes, aiming to specifically label circulating Ly6Chi monocytes.\nAfter that, we injected 1-μm Fluoresbrite YG microspheres into the retro-orbital\nsinus of MAT2ACKOApoE-/- mice and control mice. We observed significantly fewer\nlabeled monocytes recruited to plaques in MAT2ACKOApoE−/− mice compared to\ncontrols (Supplemental Figure 10C). These results further support that reduced\nCCR2 expression mediated by MAT2A knockout contributes to diminished\nmonocyte recruitment into plaques.\nIn the Results section, the sentence “Moreover, we labeled Ly6Chi monocytes with\nFluoresbrite YG microspheres and quantified their recruitment into aortic\nplaques. There were significantly fewer labeled Ly6Chi monocytes recruited into\nthe plaques of MAT2ACKOApoE−/− mice than in those of the controls\n(Supplemental Figure 10C).” has been added in the revised manuscript. (Page 15;\nLine 9-13)\nIn the Methods section, the sentences “Monocyte labeling in vivo: Circulating\nLy6Chi monocytes were labeled in vivo by retro-orbital injection of 1 μm\nFluoresbrite YG microspheres (Polysciences Inc., PA, USA) diluted in sterile PBS\n(1:4) 18 h after tail vein injection of 250 μL clodronate loaded liposomes. The\nefficiency of fluorescent beads labelling was verified 24 h after injection by flow\ncytometry of blood monocytes. After 72 h, we quantified the number of labeled\nbeads in aortic root cross-sections using fluorescence microscopy.” have been\nadded in the revised manuscript. (Page 50; Line 13-19)\nNew results have been added as Supplemental Figure 10C\nrecruitment in atherosclerosis.\nC, Representative images and quantification of fluorescent bead-labeled Ly6Chi\nmonocytes recruited into aortic root plaques of MAT2ACKOApoE-/- and\nMAT2Afl/flApoE-/- mice with an HFD. Scale bar=10 µm. Data are presented as the mean\n± SD. Unpaired Student’s t-test was used; two-tailed P values are shown, n=5.\n6. Since CCR2 expression is reduced in monocytes, one could expect also a reduced\nmonocytes egress from bone marrow and monocyte accumulation in the latter. Since\nLyz2 is also expressed in myeloid-cell progenitor cells, could the authors provide data\non whether myelopoiesis is affected by MAT2A deletion?\nResponse: We appreciate the reviewer’s critical insights. This observation aligns\nwith Comment #9 from Reviewer 1, providing valuable direction for refining our\nunderstanding of MAT2A’s role in regulating myelopoiesis and for further\nelucidating the fundamental mechanism underlying its impact on CCR2\nexpression in monocytes during atherosclerosis.\nThe differentiation of bone marrow stem cells (LSK) and progenitor cells\n(GMP/CMP) generates granulocytes and monocytes, which are essential for\nmaintaining immune homeostasis. Previous studies indicated that genetically\nperturbing key enzyme expression in myeloid progenitors—achieved through\nLyz2-Cre-mediated generation of myeloid-specific conditional knockout mice—\nmay disrupt overall bone marrow hematopoiesis and potentially accelerate\npathological progression (Circulation. 2025;151(20):1451-1466; Cell Death Differ.\n2024;31(6):697-710). To investigate whether myeloid-specific MAT2A deletion\naffects myeloid differentiation, we isolated bone marrow cells from\nMAT2ACKOApoE-/- and control mice. Flow cytometry revealed significantly\nreduced populations of LSK cells, GMPs, CMPs, and peripheral blood monocytes\nin MAT2ACKOApoE-/- mice (Supplemental Figure 8, B-F). These results\ndemonstrate that MAT2A deficiency impairs myelopoiesis and myeloid bias,\nultimately leading to diminished monocyte production. Consequently, reduced\ndeficit rather than imbalanced bone marrow egress or monocyte accumulation.\nRelevant additions have been made to the revised Results section of the\nmanuscript：\n“To investigate the effect of MAT2A knockout on myelopoiesis, we examined the\nnumbers of hematopoietic stem cells (HSCs) and circulating monocytes.\nCompared with the control group, the MAT2ACKOApoE-/- group exhibited a\nreduction in the number of hematopoietic progenitor cells, which are defined as\nlineage−Sca-1+c-Kit+ cells (LSKs). Furthermore, the numbers of myeloid\nprogenitors, including granulocyte-monocyte progenitors (GMP) and common\nmyeloid progenitors (CMP), were significantly reduced in the MAT2ACKOApoE-/-\ngroup. Meanwhile, the peripheral blood monocyte quantification was reduced in\nMAT2ACKOApoE-/- mice, indicating that MAT2A regulates bone marrow stem cell\nproliferation and myeloid bias, thereby promoting the accumulation of circulating\nmonocytes (Supplemental Figure 8, B-F).” (Page 14; Line 14-20 and Page 15; Line\n1-4)\nIn the Discussion section, the sentences have been revised as following:\n“After myeloid MAT2A knockout, there was a reduction in myelopoiesis and\nproinflammatory monocytes/macrophages, coupled with diminished migratory\ncapacity. This resulted in a substantial reduction in plaque vulnerability and\ninhibition of plaque progression.” (Page 28; Line 14-17)\nNew results have been added as Supplemental Figure 8, B-F\nproliferation and myeloid bias.\nB, Schematic of the differentiation path of hematopoietic stem cells toward monocytes\nand neutrophils. C and D, Representative gating strategy and cell count quantifications\nof LSKs (Lin-Sca-1+c-Kit+ cells), GMPs (Lin−Sca-1−c-Kit+CD34+CD16/32+ cells),\nCMPs (Lin−Sca-1−c-Kit+CD34+C16/32- cells) (n=6). E, Flow cytometry plots showing\ngating strategy and quantification for circulating monocytes (CD45+CD11b+CD115+).\nare presented as the mean ± SD. Unpaired Student’s t-test was used; two-tailed P values\nare shown. CMP, common myeloid progenitors; GMP, granulocyte-monocyte\nprogenitors.\n7. For the reviewer it remains unclear how MAT2A loss in monocytes and macrophages\naffected their intracellular metabolism. Flux analysis appear essential to address this\nquestion.\nResponse: To determine the effect of MAT2A deletion on\nmononcytes/macrophages methionine metabolism, we employed an isotope\ntracing method with 13C -methionine to directly examine the relative flux through\n5\nthe MAT2A-mediated methionine metabolism. BMDMs from MAT2ACKOApoE-/-\nand MAT2Afl/fl ApoE-/- mice were labeled with 13C -methionine and the methionine\n5\n26\nmetabolic changes were measured by targeted UHPLC-MS/MS. The results\nshowed that MAT2A deficiency primarily restricted the flux of methionine within\nthe methionine cycle in BMDMs (Supplemental Figure 4, A-C). This suggests a\nMAT2A-dependent regulation of methionine incorporation into SAM and its\ndownstream products in monocytes/macrophages.\nRelevant experimental findings have been incorporated into the original Results\nsection of the manuscript:\n“Additionally, methionine can also be recycled through the methionine salvage\npathway from the SAM-dependent polyamine biosynthesis. Utilizing 13C-labeled\nmethionine, we examined the effects of MAT2A knockout on methionine cycle as\nwell as polyamine levels. We observed that, following MAT2A depletion,\nmethionine was preferentially channeled into methylation processes rather than\npolyamine synthesis pathways (Supplemental Figure 4, A-C).” (Page 12; Line 5-\n10)\nIn addition, in the Methods section, the following sentences have been added: “13C-\nlabeled isotope metabolic flux assay: Cellular metabolic flux experiments were\nperformed based on a published study (68). Methionine is specifically labeled with\n13C (200μm, HY-NO326S5, MedChemExpress, New Jersey, USA), allowing for the\ntracking of methyl transfer in biochemical processes. The isolated BMDMs were\ncultured in a methionine-free medium for 24 h. Then, both groups of BMDMs were\nsupplemented with 13C-labeled methionine and incubated for 12 h. After\nincubation, the cells were lysed for quantitative analysis of 13C-labeled methionine\nand its metabolites. UHPLC-MS/MS analysis was used for accurate detection of\nthese metabolites.” (Page 46; Line 1-9)\nThe following new references have been cited in the revised manuscript as well:\n68. Long CP, Antoniewicz MR. High-resolution (13)C metabolic flux analysis. Nat.\nProtoc. 14, 2856-2877 (2019).\nWe have added the new results as Supplemental Figure 4\nintracellular methionine pathway metabolites.\nA, C13 labeling of methionine with respective isotope derivatives. B, Relative total 13C\nmethionine pathway metabolite enrichment. C, Relative enrichment ratios of summed\nother isotopologs normalized to metabolite-specific M+n isotopolog in BMDMs from\nMAT2ACKOApoE-/- and MAT2Afl/fl ApoE-/- mice. Data are presented as the mean ± SD.\nUnpaired Student’s t-test was used; two-tailed P values are shown, n=3.\n8. The effects of 6-OHDA and CD feeding are interesting but whether\nmonocyte/macrophage MAT2A expression is involved remains to be established. The\nauthors should perform similar experiments in Lyz2cre x MAT2Afl/fl x ApoE-/- and\ncontrol mice.\nResponse: Thank you for the reviewer’s comment. Based on your suggestions,\nwe\nhave performed additional experiments and listed as following:\n1) Response regarding the effects of MRD in MAT2ACKOApoE-/- mice:\nmonocytes/macrophages MAT2A expression in mice with MRD treatment.\nSpecifically, after 10 weeks of HFD, MAT2ACKOApoE-/- mice were administered\neither MRD or CD for 6 weeks. Unlike its effect in ApoE-/- mice, MRD failed to\nattenuate plaque progression in MAT2ACKOApoE-/- mice, suggesting that the\natheroprotective effect of methionine dietary intervention depends on its key\nmetabolic enzyme MAT2A (Supplemental Figure 17, A-C).\nThe following additions have been made to the revised Results section of the\nmanuscript:\n“Interestingly, MRD did not alter the progression of atherosclerosis in\nMAT2ACKOApoE-/- mice, demonstrating that the atheroprotective effect induced\nby dietary methionine intervention is dependent on the key metabolic enzyme\nMAT2A (Supplemental Figure 17, A-C).” (Page 21; Line 10-13)\nNew results have been added as Supplemental Figure 17\nSupplemental Figure 17. MRD reduces atherosclerosis progression in MAT2A\nknockout mice.\nA, Flowchart illustrating the experimental procedure. B and C, Representative images\nand quantification of H&E staining and Oil Red O staining in aortic roots between\nMAT2ACKOApoE-/-+CD and MAT2ACKOApoE-/-+MRD mice. Scale bar=100 µm. Data\nare presented as the mean ± SD. Unpaired Student’s t-test was used; two-tailed P values\nare shown, n=5.\n2) Response regarding the effects of 6-OHDA in MAT2ACKOApoE-/- mice:\nWe have further examined the effects of monocytes/macrophages MAT2A\nexpression in mice with 6-OHDA administration. Our findings suggest that\nMAT2A deficiency no longer attenuates atherosclerosis progression upon 6-OHDA\ntreatment. This indicates that 6-OHDA, by chemically ablating sympathetic nerve\nfibers, disrupts the norepinephrine-MAT2A pathway, thereby attenuating the\nMAT2A-mediated proatherosclerotic effects (Supplemental Figure 19, A-C).\nThe following has been inserted at the appropriate locations in the revised Results\nsection of the manuscript:\n“Furthermore, as anticipated, 6-OHDA administration under MAT2A knockout\nconditions no longer reduced atherosclerosis progression, indicating that MAT2A-\nmediated proatherosclerotic effect in macrophages is controlled by sympathetic\nnerve activation (Supplemental Figure 19, A-C).” (Page 23; Line 6-9)\nNew results have been added as Supplemental Figure 19\nSupplemental Figure 19. 6-OHDA treatment under MAT2A deletion conditions\ncould not reduce atherosclerosis progression.\nA, Schematic representation of the experimental strategy in MAT2Afl/flApoE-/- and\nMAT2ACKOApoE-/- mice with 6-OHDA as indicated and subsequent analysis. B and C,\nRepresentative images and quantification of H&E staining and Oil Red O staining in\naortic roots between 6-OHDA+MAT2Afl/flApoE-/- and 6-OHDA+MAT2ACKOApoE-/-\nmice group. Scale bar=100 µm. Data are presented as the mean ± SD. Unpaired\nStudent’s t-test was used; two-tailed P values are shown, n=6.\nIn their manuscript titled “MAT2A promotes atherosclerotic plaque vulnerability by\nmediating epigenetic reprogramming of macrophages,” Zhuo Du et al. evaluated the\nrole of Met-SAM metabolism mediated by MAT2A in the progression of\natherosclerosis. They obtained substantial data and concluded that MAT2A is highly\nexpressed in monocytes/macrophages, and its product SAM contributes to the\nupregulation of pro-inflammatory/migratory gene expression via H3K4me3, thereby\npromoting plaque vulnerability. Methionine metabolism has been found playing a\npivotal role in governing the proinflammatory immunological status of macrophages\nby regulating the epigenetic switch and conferring the metabolic demand. This study\nadds new evidence to understanding the methionine metabolism in epigenetic\nregulation in atherosclerosis. The study is well designed, and the narration is\nstraightforward.\nWhile this represents a novel link between MAT2A and atherosclerosis, the functional\nmechanisms of the MAT2A-SAM axis in vulnerable plaques still need further validated.\nMany critical questions remain unanswered. Specifically:\n1. Introduction: The literatures provided in the introduction that draws forth the specific\nscientific question are about methionine metabolism. There are some observations\nimplying the link of MAT2A with atherosclerosis, are more relevant to\njustify the present study.\nResponse: Thank you for the reviewer's valuable suggestions. We conducted a\nliterature mining analysis to evaluate the potential association between MAT2A\nand atherosclerosis. Despite the absence of a direct causal link identified, MAT2A\nmay represent a potential critical regulatory node in atherosclerosis pathogenesis\nby indirectly modulating key processes, including proinflammatory macrophages\nactivation, ROS accumulation in ferroptosis induction, SAM-dependent DNA\ndamage and so on, that drive atherogenesis.\nWe have added the Introduction section with following sentences:\n“…Accumulating evidence indicates that MAT2A regulates macrophage\ninflammation, ROS accumulation in ferroptosis induction, and SAM-dependent\nhypomethylation-driven DNA damage, all of which are core drivers of\natherogenesis (12, 20, 21).…” (Page 7; Line 9-12)\nThe following new references have been cited in the revised manuscript as well:\n12. Yu W, et al. One-Carbon Metabolism Supports S-Adenosylmethionine and Histone\nMethylation to Drive Inflammatory Macrophages. Mol. Cell 75, 1147-1160.e1145\n(2019).\n20. Xia C, et al. Methionine-SAM metabolism-dependent ubiquinone synthesis is\n31\ncrucial for ROS accumulation in ferroptosis induction. Nat. Commun. 15, 8971\n(2024).\n21. Li F, et al. Blocking methionine catabolism induces senescence and confers\nvulnerability to GSK3 inhibition in liver cancer. Nat. Cancer 5, 131-146 (2024).\n2. Specificity of SAM function: Although SAM is well-known as a methyl donor, it\nalso plays an important role in polyamine synthesis. The authors need to determine the\nspecificity of SAM's methylation function and whether SAM can exert its effects\nthrough polyamine synthesis.\nResponse: To test whether deletion of MAT2A affects monocytes/macrophages\nfunction and atherosclerosis progression through specific SAM methylation, we\nexamined intracellular methionine pathway related metabolite levels in BMDMs\nisolated from MAT2ACKOApoE-/- and MAT2Afl/flApoE-/- mice by flux analysis. The\nresults demonstrated that MAT2A deficiency triggers a specific collapse of the\nmethylation pathway, characterized by significant reductions in SAM, SAH,\nand\nHCY, while spermidine and spermine levels in the polyamine synthesis pathway\nremained unchanged (Supplemental Figure 4, A-C). This phenomenon likely\narises from the heightened sensitivity of methylation processes to SAM\nfluctuations (Mol Cell. 2021;81(10):2076-2093). Furthermore, macrophages can\nsustain polyamine stability through exogenous polyamine uptake (Immunity.\n2024;57(8):1796-1811). Collectively, these findings indicate that SAM is\npreferentially channeled into the methylation pathway during atherosclerosis\nprogression.\nIn the Results section, the sentence “Additionally, methionine can also be recycled\nthrough the methionine salvage pathway from the SAM-dependent polyamine\nbiosynthesis. Utilizing 13C-labeled methionine, we examined the effects of MAT2A\nknockout on methionine cycle as well as polyamine levels. We observed that,\nfollowing MAT2A depletion, methionine was preferentially channeled into\nmethylation processes rather than polyamine synthesis pathways (Supplemental\nFigure 4, A-C).” has been added in the revised manuscript. (Page 12; Line 5-10)\nIn the Methods section, the sentences “13C-labeled isotope metabolic flux assay:\nCellular metabolic flux experiments were performed based on a published study\n(68). Methionine is specifically labeled with 13C (200μm, HY-NO326S5,\nMedChemExpress, New Jersey, USA), allowing for the tracking of methyl transfer\nin biochemical processes. The isolated BMDMs were cultured in a methionine-free\nmedium for 24 h. Then, both groups of BMDMs were supplemented with 13C-\nlabeled methionine and incubated for 12 h. After incubation, the cells were lysed\nfor quantitative analysis of 13C-labeled methionine and its metabolites. UHPLC-\nMS/MS analysis was used for accurate detection of these metabolites.” have been\nadded in the revised manuscript. (Page 46; Line 1-9)\nThe following new references have been cited in the revised manuscript as well:\n32\n68. Long CP, Antoniewicz MR. High-resolution (13)C metabolic flux analysis. Nat.\nProtoc. 14, 2856-2877 (2019).\nNew results have been added as Supplemental Figure 4\nSupplemental Figure 4. MAT2A deficiency predominantly led to the reduction in\nintracellular methionine pathway metabolites.\nA, C13 labeling of methionine with respective isotope derivatives. B, Relative total 13C\nmethionine pathway metabolite enrichment. C, Relative enrichment ratios of summed\nother isotopologs normalized to metabolite-specific M+n isotopolog in BMDMs from\nMAT2ACKOApoE-/- and MAT2Afl/fl ApoE-/- mice. Data are presented as the mean ± SD.\nUnpaired Student’s t-test was used; two-tailed P values are shown, n=3.\n3. Specificity of SAM-H3K4me3 in gene expression for plaque vulnerability: This\nconcern specially goes to Figure 4. When H3K4me3, but not H3K36me3, is globally\ndownregulated (A), the authors should also detect the enrichment of H3K36me3 to\nthe promoters of target genes as control. Also, what is the histone modification status of IL-\n33\n1β promoter since the literature documented H3K36me3 underpinning IL-1β\nupregulation in LPS-exposed macrophages. What defines/discriminates the\ndifference of the types of histone methylations, which should be well discussed since this is the\ndown-to-the-bottom\nmechanism for this study.\nResponse: We appreciate the reviewer’s expert comments, which have helped\nenhance the robustness of the proposed mechanisms in our manuscript.\n1) Response regarding the enrichment of H3K36me3 to the promoters of target\ngenes:\nWe utilized the UCSC Genome Browser to predict the enrichment of H3K36me3\nmodifications to the promoters of AIM2, CCL2, and MMP9 genes in BMDMs.\nFollowing normalization, we observed that H3K36me3 is predominantly enriched\nin the gene body regions of these target genes rather than promoter regions.\nFurther ChIP-qPCR analysis revealed H3K36me3 enrichment was significantly\nreduced specifically at the CCL2 promoter following MAT2A deletion, whereas\nAIM2 and MMP9 exhibited no remarkable differences between the two groups.\nMAT2A knockout selectively reduces H3K36me3 enrichment at the CCL2\npromoter.\nA, UCSC genome browser snapshots showing the histone modifications of H3K36me3\non AIM2, CCL2 and MMP9 in BMDMs. The region indicated by the red line\ncorrespond to the promoters of the target genes. B, H3K36me3 occupancy analysis\nby ChIP-qPCR of AIM2, CCL2, and MMP9 in BMDMs from MAT2ACKO and\nMAT2Afl/fl mice (n=5). Data are presented as the mean ± SD. Unpaired Student’s t-test\nwas used; two-tailed P values are shown.\n2) Response regarding the histone modification status of IL-1β promoter:\nBased on the reviewers' comments, we conducted a systematic analysis of the effect\nof MAT2A knockout on IL-1β histone modifications. Specifically, our initial in\n34\nvitro PCR experiments in this study revealed that IL-1β expression can be\nregulated by MAT2A in a SAM-dependent manner (Figure 3, G and I, and\nSupplemental Figure 10I). According to the reviewer’s suggestion, we further\nperformed RNA-seq on plaque macrophages, which demonstrated significant\nenrichment of the IL-1β pathway among DEGs following MAT2A knockout\n(Supplemental Figure 7F). This was corroborated by PCR confirming reduced IL-\n1β levels in plaque macrophages (Supplemental Figure 9B). Furthermore, binding\ndistributions revealed that H3K4me3 exhibited pronounced enrichment at the\nstart sites of transcription initiation within IL-1β promoter region, in contrast to\nH3K36me3 which primarily enriched across gene bodies to facilitate\ntranscriptional elongation (Supplemental Figure 12D). Subsequent CUT&Tag\nanalysis revealed downregulation of H3K4me3 enrichment at the IL-1β promoter\nregion upon MAT2A knockdown. GO enrichment analysis indicated significant\nenrichment of inflammatory pathways regulating IL-1β production (Figure 4E).\nTherefore, the histone modification status of IL-1β promoter is mainly enrichment\nof H3K4me3 modulated by MAT2A.\nThe relevant details have been added to the Results section of the manuscript:\n“…We further observed MAT2A deletion reduced proinflammatory and\nchemotactic gene expression in plaque macrophages (Supplemental Figure\n9B)…’’(Page 15; Line 6-8)\n“…Further gene enrichment analysis revealed that the downregulated genes with\ndifferential H3K4me3 were involved in inflammatory response including IL-1β\nproduction and migratory function (Figure 4, E and F). Simultaneously, we\nobserved that H3K4me3 modification at the IL-1β promoter was reduced upon\nMAT2A depletion (Supplemental Figure 12D), which is consistent with previous\nreports indicating that SAM regulates IL-1β expression (12)…” (Page 18; Line 5-\n10)\nThe following new references have been cited in the revised manuscript as well:\n12. Yu W, et al. One-Carbon Metabolism Supports S-Adenosylmethionine and Histone\nMethylation to Drive Inflammatory Macrophages. Mol. Cell 75, 1147-1160.e1145\n(2019).\nMAT2Afl/fl mice treated with or without SAM (Figure 3G), plaque macrophages from\nMAT2Afl/flApoE-/- and MAT2ACKOApoE-/- mice (Supplemental Figure 9B), and THP-\n1 (Figure 3I) or RAW264.7 cells (Supplemental Figure 10I) were transfected with\nsiNC or siMAT2A. Supplemental Figure 7F, GO term enrichment analysis of\ndownregulated DEGs between MAT2Afl/flApoE-/- and MAT2ACKOApoE-/- mice. Figure\n4E, Bubble charts of GO analysis according to downregulated genes with decreased\nH3K4me3 modification. Supplemental Figure 12D, IGV tracks revealing the results\nof CUT&Tag reads (H3K4me3 binding) distributions in IL-1β.\n3) Response regarding the difference of the types of histone methylations:\nResponse: As correctly noted by the reviewer, prior studies have established the\nresponsiveness of H3K36me3-mediated IL-1β regulation during LPS-induced\ninflammation. However, LPS achieves epigenetic reprogramming of IL-\n1β through integrated modulation of the pentose phosphate pathway, serine\nsynthesis pathway, and one-carbon metabolism (Mol Cell. 2019;75(6):1147-1160).\nIn contrast, our study specifically perturbed MAT2A, a pivotal enzyme within the\nmethionine cycle branch of one-carbon metabolism, and examined its downstream\nmetabolic effects. However, emerging evidence contradicts this finding by\ndemonstrating that LPS stimulation downregulates the methyltransferase of\nH3K36me3 SETD2 expression in macrophages. This downregulation likely\ncontributes to reduced H3K36me3 enrichment at inflammatory target genes in\nsynergistically induce epigenetic reprogramming for inflammatory gene\nexpression through multiple pathways. Disruption of key links in the methionine\npathway in the presence of LPS may not be the only pathway leading to\nH3K36me3 degradation. In addition, the activity of histone methyltransferases\nalso plays an important role in determining the type of histone methylation. For\nexample, SETD2 is able to maintain its activity by means of other metabolic\npathways (OXPHOS metabolism, phospholipid metabolism, etc.) and thus has a\ngreater buffering capacity against the fluctuations in SAM (Cancer Cell.\n2024;42(5):869-884; Proc Natl Acad Sci U S A. 2024;121(8):e2314561121).\nMoreover, the differences in the types of histone methylation may be determined\nby the dependence on SAM and the sensitivity to alterations in the methionine\nmetabolic environment. Specifically, when intracellular SAM levels decreased due\nto environmental methionine depletion, H3K4me3 reduction was more\npronounced than that of other histone methylations (such as H3K36me3, H3K9me\n3 and H3K27me3) (Cell Metab. 2020;31(2):250-266). Finally, H3K4me3 tends to be\nenriched at gene promoters, which is directly related to transcription initiation of\nactive target genes and ensures transcriptional precision at key cell\nidentity/function genes (Trends Cell Biol. 2025;35(2):115-128; Cell.\n2014;158(3):673-88). Whereas H3K36me3, also transcriptive active, tends to be\nenriched at gene body and its function is more related to transcriptional elongation.\nTherefore, the direct regulatory associations with key effector genes are generally\nlimited (Nat Rev Mol Cell Biol. 2012;13(2):115-126).\nIn the Discussion section, the sentences “In our study, although H3K36me3\nexpression decreased following MAT2A depletion, its reduction was less\npronounced compared to H3K4me3. It has been suggested that the SETD2 was\nresponsible for H3K36me3 potentially maintaining its activity through alternative\nmetabolic pathways, thereby exhibiting greater buffering capacity against SAM\nfluctuations (51,52). While H3K4me3 demonstrates stronger dependence on SAM\navailability and more heightened sensitivity to alterations in methionine metabolic\nstatus, compared to other histone modifications such as H3K36me3 and\nH3K27me3 (53). This observation was consistent with the elevated levels of SAM\nfound in both TCFA-positive patients and ApoE-/- mice in this study,\nunderscoring\nthe dependence of macrophages on sufficient methionine metabolism to generate\nSAM for H3K4me3 methylation. Additionally, this phenomenon might be\nattributed to H3K4me3 enrichment at promoter regions, which enables more\ndirect regulation of transcriptional initiation at target genes, while H3K36me3\npredominantly localizes to gene body regions and facilitates transcriptional\nelongation (54, 55, 56).” have been added in the revised manuscript. (Page; Line -)\nThe following new references have been cited in the revised manuscript as well:\n51. Niu N, et al. Tumor cell-intrinsic epigenetic dysregulation shapes cancer-associated\nfibroblasts heterogeneity to metabolically support pancreatic cancer. Cancer Cell\n42, 869-884.e869 (2024).\n37\n52. Chen Y, Chen K, Zhu H, Qin H, Liu J, Cao X. Methyltransferase Setd2 prevents T\ncell-mediated autoimmune diseases via phospholipid remodeling. Proc. Natl. Acad.\nSci. USA 121, e2314561121 (2024).\n53. Roy DG, et al. Methionine Metabolism Shapes T Helper Cell Responses through\nRegulation of Epigenetic Reprogramming. Cell Metab. 31, 250-266.e259 (2020).\n54. Wang H, Helin K. Roles of H3K4 methylation in biology and disease. Trends Cell\nBiol. 35, 115-128 (2025).\n55. Benayoun BA, et al. H3K4me3 breadth is linked to cell identity and transcriptional\nconsistency. Cell 158, 673-688 (2014).\n56. Wagner EJ, Carpenter PB. Understanding the language of Lys36 methylation at\nhistone H3. Nat. Rev. Mol. Cell Biol. 13, 115-126 (2012).\n4. Independence or synergy with Hcy: Is the mechanism by which MAT2A inhibition\nor methionine deprivation regulates macrophage function completely independent of\nHcy, or is it synergistic with Hcy? Further experiments involving AHCY\nregulation or Hcy supplementation are needed to clarify this.\nResponse: As the reviewer noted, Hcy, as a key metabolite in the methionine cycle,\nhas been indeed previously demonstrated to be associated with cardiovascular\ndisease risk (BMJ. 2002;325(7374):1202). However, in atherosclerotic diseases, the\nstudies have shown that elevated Hcy levels tend to represent an epiphenomenon\nrather than a direct causative factor in disease pathogenesis (Eur J Prev Cardiol.\n2021;28(2):227–234; N Engl J Med. 2006;354(15):1567-77). Similarly, our findings\nrevealed that the effect of MAT2A on atherosclerosis of MAT2A could not be\nreversed by Hcy reduction (Supplemental Figure 6, A-C). Subsequently, according\nto the comment from the reviewer, we have now included measurements of\ninflammatory and migratory capacities in monocytes/macrophages from FIDAS-\n5-fed mice, with or without Hcy supplementation. We found that there were no\nsignificant differences in AIM2, CCL2, or MMP9 mRNA levels between the two\ngroups (Supplemental Figure 15A). Also, the cell migration ability measured by\nTranswell assay did not show differences within the groups (Supplemental Figure\n15B). These findings provide direct experimental evidence that MAT2A regulates\nmonocytes/macrophages function through Hcy-independent mechanisms, further\nsupporting the bystander role of Hcy in the disease pathogenesis.\nIn the Result part, we have added the following content:\n“Further analysis following HCY supplementation in mice with FIDAS-5\nmanagement revealed no significant alterations in monocytes/macrophages\ninflammatory and migratory function, suggesting MAT2A inhibition regulates\nmacrophage function independent of Hcy (Supplemental Figure 15, A and B).”\n(Page 20; Line 7-11)\nIn the Methods section, the following sentence has been added:\n38\n“ApoE-/-mice were received standard or DL-Hcy (1.8 g/L, 6038-19-3, Sigma-\nAldrich, St. Louis, MO, USA) -containing drinking water for 4 weeks to establish\nthe Hcy supplementation model as previously described (65).” (Page 39; Line 16-\n18)\nThe following new references have been cited in the revised manuscript as well:\n65. Du X, et al. B cell-derived anti-beta 2 glycoprotein I antibody mediates\nhyperhomocysteinemia-aggravated hypertensive glomerular lesions by triggering\nferroptosis. Signal Transduct. Target Ther. 8, 103 (2023).\nNew results have been added as Supplemental Figure 15\nSupplemental Figure 15. FIDAS-5 regulates monocytes/macrophages function\nindependent of Hcy.\nA, AIM2, CCL2, and MMP9 mRNA levels were analyzed by qRT-PCR from peripheral\nblood monocytes. B, Transwell assay and quantification (Scale bar=100 µm) of\nBMDMs isolated from FIDAS-5 and FIDAS-5+Hcy group mice. Data are presented as\nthe mean ± SD. Unpaired Student’s t-test was used; two-tailed P values are shown, n=6.\n5. SAM supplementation in mouse experiments: In Figure 5, after treating mice with\nMAT2A inhibition or methionine deprivation, it is recommended to supplement SAM\nat key experimental time points to validate the findings.\nexperiments. We administered SAM (2.5 mg/kg) via intraperitoneal injection daily\nfor 5 weeks, following one week post FIDAS-5 treatment to inhibit MAT2A in\nApoE-/- mice (Supplemental Figure 14A). The results showed that the\narteriosclerosis progression attenuated by FIDAS-5 was restored by SAM\nsupplementation (Supplemental Figure 14, B and C), suggesting that SAM is an\nessential metabolite in MAT2A-mediated atherosclerosis.\nIn the Result part, we have added the following content:\n“As expected, additional supplementation of SAM under conditions of MAT2A\ninhibition by FIDAS-5 would further exacerbate atherosclerosis (Supplemental\nFigure 14, A-C), indicating MAT2A promotes atherosclerosis progression by\nregulating SAM generation.” (Page 20; Line 4-7)\nIn the Methods section, the sentences “For SAM supplementation in experiments,\nmice were injected intraperitoneally with 2.5 mg/kg SAM (S5109, Selleck, USA),\nwhich was dissolved in sterile PBS daily for 5 weeks.” have been added in the\nrevised manuscript. (Page 40; Line 2-4)\nNew results have been added as Supplemental Figure 14\nSupplemental Figure 14. SAM restoration aggravates atherosclerosis progression.\ntreatment as indicated. B, SAM and SAH concentrations as well as SAM/SAH ratio for\nmice monocyte cell lysate based on mass spectrometry (n=3). C, Representative images\nand quantification of H&E staining and Oil Red O staining in aortic roots for FIDAS-5\nmice and FIDAS-5+SAM mice (n=6). Scale bar=100 µm. Data are presented as the\nmean ± SD and comparisons were made using unpaired Student’s t-test; the two-tailed\nP-values are shown.\n6. Role of mTOR downstream transcription factors: Although the authors demonstrated\nthat the mTOR-c-Myc axis promotes MAT2A expression, other classic transcription\nfactors downstream of mTOR, such as HIF1α and PPARγ, should also be considered.\nFurther experiments are needed to confirm whether mTOR acts through c-Myc.\nResponse: Thank you for the reviewer's suggestions. We agree with the reviewer\nthat other classic transcription factors downstream of mTOR should also be\nconsidered in our study. Therefore, we have further supplemented the analysis of\nthe expression of MAT2A after transfection treatment with siRNA targeting\nmTOR's downstream transcriptional factors (c-MYC, HIF-1α, PPARγ, and\nSREBP) (Mol Cell. 2021;81(10):2076-2093). The results demonstrated that only c-\nMYC knockdown significantly reduced MAT2A expression, while other factors\nshowed no notable effects. Subsequent experiments revealed that following\ninsulin-induced physiological activation of the mTOR pathway (Mol Cell.\n2020;79(1):43-53), the upregulated expression of MAT2A was reversed upon c-\nMYC silencing, suggesting that mTOR regulates MAT2A expression specifically\nthrough c-MYC-dependent transcriptional control (Supplemental Figure 20, A\nand B).\nThe following content has been inserted at the relevant locations in the revised\nResults section of the manuscript:\n“To delineate mTOR-dependent transcriptional regulation of MAT2A, we\nassessed candidate downstream transcription factors. Among them, c-MYC\ndepletion specifically reduced MAT2A expression (Supplemental Figure 20A).”\n(Page 23; Line 15-18)\n“In c-MYC-deficient BMDMs, the insulin-induced increase in MAT2A expression\ndriven by physiological mTOR pathway activation (38) was abolished,\ndemonstrating MAT2A transcriptional dependence on the mTOR-c-MYC\nsignaling axis (Supplemental Figure 20B).” (Page 23; Line 19-20 and Page 24; Line\n1-2)\nIn the Methods section, the sentences “To activate the mTOR signaling pathway,\nBMDMs were stimulated with insulin (100 nM, PB180432, Procell, Wuhan, China)\nfor 15 h (38).” have been added in the revised manuscript. (Page 43; Line 16-17)\nThe following new references have been cited in the revised manuscript as well:\n38. Villa E, et al. mTORC1 stimulates cell growth through SAM synthesis and m(6)A\n41\nmRNA-dependent control of protein synthesis. Mol. Cell 81, 2076-2093.e2079\n(2021).\nThe new results have been added as Supplemental Figure 20, A and B\nSupplemental Figure 20. MAT2A expression is specifically regulated by the\nmTOR-c-MYC axis.\nA, BMDMs were transfected with siRNAs targeting different transcription factors, and\nMAT2A protein levels were assessed by immunoblots. B, MAT2A mRNA in BMDMs\ntransfected with the indicated sic-MYC followed by 100nM insulin. Data are presented\nas the mean ± SD. Two-way ANOVA was used; the adjusted P-values are shown, n=5.\n7. Statistical figure labeling: In Figure 2B or 2D, there is a lack of labeling for the\nexperimental groups in the statistical charts. Similar issues are present throughout the\nmanuscript and should be corrected.\nResponse: We sincerely appreciate the reviewer's meticulous attention. In\naccordance with this comment, we have incorporated group-specific labels in each\nfigure to more directly illustrate the grouping categories throughout the\nmanuscript.\nThe specific corrections were shown as follows:\nG, Quantification of plaque area, necrotic core, and collagen (n=10).\nFigure 3. Knockdown of MAT2A reduces the inflammatory and migratory\ncapacity of monocytes/macrophages.\nG, The mRNA levels of IL-1β, IL-6, INOS measured by qRT-PCR (n=6).\nFigure 5. Inhibition of MAT2A and methionine dietary intervention suppress\natherosclerotic lesions formation.\nE, Quantification of Oil Red O-stained aortic roots (n=8).\n8. Reagent descriptions: For reagents mentioned for the first time, such as 6-OHDA,\ntheir full names, functions, and references should be provided. Similar issues should be\ncorrected throughout the manuscript.\nrevised the manuscript to provide full chemical names, functions, and references\nfor all reagents at their first mention. These corrections have been systematically\nimplemented throughout the text.\nRevisions include:\n“To clarify whether dietary methionine intervention is effective for atherosclerosis\ntreatment, ApoE-/- mice were fed either a methionine-restricted diet (MRD) (17,28)\nor a control diet after an HFD (Figure 5J).” (Page 20; Line 13-15),\n“We further administered 6-hydroxydopamine (6-OHDA) to deplete\nnorepinephrine (33, 34) in ApoE-/- mice and found significantly reduced monocytes\nMAT2A expression and methionine metabolism levels (Figure 6D and\nSupplemental Figure 18B).” (Page 22; Line 18-19),\n“Furthermore, norepinephrine promoted MAT2A expression, while inhibition of\nmTOR via rapamycin (RAPA) (39) blocked this effect.” (Page 24; Line 2-3) and so\non.\nThe manuscript by Du et al described a role of Methionine adenosyltransferase Ⅱ alpha (MAT2A) in mediating the plaque\nstability through epigenetic regulation of macrophage metabolic reprogramming. Overall, the study is well designed and\nexecuted, and the insights from this study likely to shed light on the molecular mechanism of atherosclerosis. The major\nconcerns is the disconnect of the complexity of methionine metabolism among different cell types within the\nmicroenvironment of atherosclerotic plaques. In particular, the circulating methionine levels may affect other pathways other\nthan the DNA methylations, yet the MAT2A inhibition or dietary manipulation of methionine levels has such a dramatic effect\non atherosclerosis. Below are some minor concerns:\n1. In Fig. 1B, the circulating levels of methionine appears too high comparing to the\nprevious reports.\nResponse: In the present study (Figure 1B), human peripheral blood monocytes\nwere isolated from patient blood samples and subjected to targeted mass\nspectrometry. Our aim was to detect cellular metabolite levels, not circulating\nlevels. Strict quality control procedures were implemented throughout the\nanalytical process to ensure data reliability and enable accurate quantification of\nthe concentration of methionine metabolites in monocytes. It is important to note\nthat the levels of metabolites detected within cells differ from circulating\nmetabolite levels because they are regulated by cell-surface transporters (e.g., SLC\nfamily) and key metabolic enzymes (Nature. 2020;585(7824):277-282; Cancer Cell.\n2021;39(2):209-224). Furthermore, metabolic activity differs significantly among\nvarious cell types. This may explain the differences in metabolite concentrations\nbetween our study and previous reports. We hope this response alleviates your\nconcerns.\n2. In Figure 3E, how supplementation of SAM will get into the cell and be responsible\nfor the observed phenotypes.\nResponse: We appreciate the reviewer's insightful comment regarding the cellular\nuptake of supplemented SAM. To directly address this concern, we performed\ntargeted metabolomics analysis to quantify intracellular SAM levels in\nmacrophages in BMDMs from three groups (MAT2Afl/fl, MAT2ACKO,\nMAT2ACKO+SAM) shown in Figure 3D (formerly Figure 3E). We observed\nthat intracellular SAM levels increased significantly following SAM\nsupplementation, even exceeding those in MAT2Afl/fl group, demonstrating\nefficient uptake of exogenous SAM in BMDMs. Concurrently, SAM uptake by\nmacrophages also increased the methyl index (Supplemental Figure 10F),\n45\nmechanistically manifested by restored H3K4me3 modification at promoters of\nproinflammatory target genes (Figure 4H), thereby providing epigenetic\nreprogramming support for their polarization toward the inflammatory\nphenotype. These findings indicate that SAM uptake enhances methylation\npotential, thereby reprogramming macrophages toward a pro-inflammatory\nphenotype. This establishes a causal link between SAM bioavailability and\nmacrophage inflammatory responses.\nIn addition, we added the following sentences to the Results section: “…The\nabsence of MAT2A significantly reduced the proportion of proinflammatory\n(CD11b+CD86+) macrophages and intracellular methionine metabolism, which\nwas reversed by SAM supplementation, indicating the regulatory role of MAT2A\non proinflammatory macrophages depends on generating SAM (Figure 3D and\nSupplemental Figure 10F)…”(Page 15; Line 1 and Page 16; Line 1-4)\nNew results have been added as Supplemental Figure 10F\nSupplemental Figure 10. MAT2A deletion diminishes the monocytes/macrophages\nrecruitment in atherosclerosis.\nF, LC-MS/MS was utilized to measure intracellular SAM, SAH, and SAM/SAH levels.\nData are presented as the mean ± SD and comparisons were made using one-way\nANOVA; the two-tailed P-values are shown, n=3.\n3. Fig.4A, quantification of the bands is needed.\nResponse: The quantification of Fig 4A is presented below the immunoblots\nimages.\nMAT2Afl/fl and MAT2ACKO mice (n=3).\n4. In Fig. 7A, how the 100 patients are statistically justified in the validation cohort?\nResponse: In this study, of the 845 patients enrolled in the discovery cohort,\n237\npatients had at least one TCFA in all 3 vessels, while the remaining 608 patients\nhad no evidence of TCFA. 100 paired TCFA-positive and -negative patients\nmatched for age, gender, traditional coronary risk factors (i.e., diabetes mellitus,\ndyslipidaemia, current smoking, hypertension, and CKD), and concomitant statin\ntherapy were selected. Addressing the reviewers’ concerns, we computed post-\nmatching standardized mean differences (SMDs) to assess balance adequacy.\nAll\nCohen’s d were <0.2 (absolute values of d: small effect d < 0.2, medium effect d\nbetween 0.2 and 0.8, large effect d > 0.8 [Eur Heart J. 2023;44(38):3892-3907]),\nindicating well-balanced key covariates in TCFA-positive and TCFA-negative\npatients in the validation cohort (Supplemental Table 8). In the discovery cohort,\nidentical evaluation revealed well-balanced matching between TCFA-positive and\nTCFA-negative patients (Supplemental Table 2). This approach effectively\nmitigated baseline confounding risks and substantiated the statistical rigor of our\npropensity score matching framework.\nWe have described this in the revised manuscript: “Between January 2018 and\nDecember 2019, there were 845 stable patients were eligible for pancoronary OCT\nimaging and 100 paired patients were selected for age, gender, traditional\ncoronary risk factors and concomitant statin therapy as the validation cohort after\npairwise comparison…” (Page 24; Line 19-20 and Page 25; Line 1-2)\n“…100 matched patients had similar clinical characteristics and well balanced as\nshown by the standardized mean difference (SMDs) of the baseline characteristics\n(Supplemental Table 8)…” (Page 25; Line 6-8)\nsizes Cohen’ s d (standardized mean difference [SMD]) for pairwise comparisons\nwas reported to evaluate the magnitude of the effect size d (60).”\nThe following new references have been cited in the revised manuscript as well:\n60. Meteva D, et al. Toll-like receptor 2, hyaluronan, and neutrophils play a key role\nin plaque erosion: the OPTICO-ACS study. Eur. Heart J. 44, 3892-3907 (2023).\nNew results have been added as Supplemental Table 8\nSupplemental Table 8: Clinical baseline characteristics of patients which monocytes\nwere used for targeted metabolomics\nTCFA-positive TCFA-negative\npatients patients d\n(n=100) (n=100)\nAge (years) 56.3 ± 10.6 56.4 ± 11.7 0.00\nMale (n;%) 77 (77.0) 76 (76.0) 0.02\nCoronary risk factors\nDiabetes mellitus (n;%) 22 (22.0) 21 (20.0) 0.02\nDyslipidaemia (n;%) 57 (57.0) 53 (53.0) 0.08\nCurrent smoking (n;%) 53 (53.0) 52 (52.0) 0.02\nHypertension (n;%) 45 (45.0) 43 (43.0) 0.04\nCKD (n;%) 8 (8.0) 8 (8.0) 0.00\nConcomitant medication\nStatin before admission (n;%) 11 (11.0) 10 (10.0) 0.03\nLaboratory data\nTotal cholesterol (mg/dl) 175.7 173.9 0.17\n(152.0-211.8) (147.7-197.6)\nTriglyceride (mg/dl) 115.2 (85.5-163.0) 107.1 (78.3-159.4) 0.06\nLDL-C (mg/dl) 109.4 ± 33.2 105.9 ± 32.4 0.10\nHDL-C (mg/dl) 50.1 (43.7-60.7) 48.8 (43.4-58.0) 0.15\nHbA1c (%) 5.8 (5.4-6.2) 5.7 (5.5-6.2) 0.08\nhs-CRP (mg/l) 1.9 (1.1-5.0) 1.8 (1.0-5.5) 0.06\nValues shown are n (%), mean ± SD, or median with IQR.\nStandardized mean differences (SMDs, d) for continuous baseline characteristics.\nCKD, chronic kidney disease; HDL-C, high-density lipoprotein cholesterol; hs-CRP,\nhigh-sensitive C-reactive protein; LDL-C, low-density lipoprotein cholesterol; TCFA,\nthin-cap fibroatheroma.\n48\n5. The authors claimed measurements of methionine, SAM, SAH as a metabolomics. It\nis a routine mass spectrometry method not a metabolomic study.\nResponse: We appreciate the reviewer’s comment. We have revised the sections\nerroneously described as \"metabolomics\" to \"mass spectrometry\" in the\nmanuscript. The specific modifications are listed as follows:\n“…Here, we performed untargeted metabolomics and mass spectrometry to\ndemonstrate that the methionine metabolism of monocytes was associated with\nvulnerable plaque (thin-cap fibroatheroma [TCFA]) identified by pancoronary\noptical coherence tomography.…” (Page 4; Line 5-8)\n“…Further targeted mass spectrometry revealed that TCFA-positive patients had\naltered methionine metabolic profiles (i.e., methionine, SAM, and SAH) compared\nwith their counterparts (Figure 1D, and Supplemental Figure 1C)…” (Page 9; Line\n16-18)\n“…Circulating monocyte methionine metabolism levels were determined by\ntargeted mass spectrometry and serum norepinephrine levels by ELISA (Figure\n7A)…” (Page 25; Line 2-4)\n“…and the detection of monocytes methionine metabolism with targeted mass\nspectrometry and serum norepinephrine with ELISA…” (Page 32; Line 4-6)\n“Mass spectrometry and metabolite quantification” (Page 46; Line 11)\n“B, SAM and SAH concentrations as well as SAM/SAH ratio for mice monocyte\ncell lysate mass spectrometry samples (n=6).” (Page 74; Line 1-2)\n6. In the experiment, why the authors use high fat diets instead of Western diet to feed\nApoE KO mice?\nThere are some differences in these two diets in the context of atherosclerosis. In most\nstudies, WD is used rather than high fat diet\nResponse: Thank you for reviewer’s valuable suggestion. Existing literature\nindicates that WD, due to its high carbohydrate content, induces deficiencies in\nmicronutrients (particularly B vitamins) - key cofactors that substantially\nmodulate Hcy levels in methionine metabolism. This nutritional perturbation\nwould confound our assessment of methionine cycle activity (Nutrients.\n2021;13(11):3914; Int J Obes (Lond). 2019;43(2):219-232). Moreover, compared to\nHFD, WD exerts more pronounced disruptive effects on gut microbiota\ncomposition, thereby introducing additional metabolic variables that may act as\nsignificant confounding factors in metabolic studies (Nutrients. 2023;15(12):2749;\nNat Microbiol. 2021;6(7):874-884). Therefore, we selected HFD for atherosclerosis\nmodel in this study.\nRE: Manuscript Number: # NCOMMS-25-02859A\nMAT2A promotes atherosclerotic plaque vulnerability by mediating epigenetic\nreprogramming of macrophages\nTo the Editor and the Reviewers:\nWe extend our appreciation to the editor and reviewers for their constructive comments\non our manuscript. We have provided our point-by-point responses below and have\nrevised the manuscript files according to the checklist. The manuscript has been\nreformatted according to the journal's requirements, and we sincerely hope that the\nrevised version meets the standards for publication.\nReviewer #3 (Remarks to the Author):\n1. In multiple supplemental figures (e.g., Suppl Fig 1C/D, 2A/B), statistical significance\nis marked with symbols like “*” or “#”, but the corresponding definitions of these\nsymbols (e.g., “*p<0.05, **p<0.01”) are missing in the figure legends. This forces\nreaders to cross-reference the main text, reducing readability.\nResponse: We sincerely thank the reviewer for their meticulous attention to detail.\nInstead, the exact P-values (e.g., P = 0.0078) were directly displayed on the figures\nthemselves. Therefore, the issue of missing symbol definitions in the legends does\nnot apply to these particular figures, as the statistical information was already\nfully embedded within the figure panels. To better illustrate our findings, we have\nprepared the following figure which we believe provides clarity:\nSupplemental Figure 1. Patients and mice with advanced atherosclerotic plaques\nhave altered methionine metabolic profiles.\nmetabolomics from patients with TCFA-positive or TCFA-negative (C) (n=38 subjects)\nand ApoE-/- mice or controls (D) (n=5 individual animals) samples.\nSupplemental Figure 2. Increased MAT2A-mediated methionine metabolism in\nmonocytes/macrophages is correlated with atherosclerosis progression.\nThe mRNA levels analyzed by qRT-PCR in peripheral blood monocytes isolated from\npatients with TCFA-positive or TCFA-negative (A) (n=38 subjects) and ApoE-/- mice\nor controls (B) (n=5 individual animals) samples.\n2. In Suppl Tables 1, 4, 7, and 10, the P-value columns are misaligned with their\ncorresponding variables (e.g., in Suppl Table 1, the P-value for “Male (n;%)” is offset\nfrom its row). This causes confusion about which variable each P-value corresponds to.\nResponse: We meticulously re-examined all Supplementary Tables in the original\nsubmission. We found that the P-values and corresponding variables were\ncorrectly aligned according to standard formatting.\nThe final version of Supplemental Table 1, as it appears in the PDF, is presented\nbelow for clarity:\n2\n3. In Figure 2E and Figure 6E, the legends state “Scale bar = 200” and “Scale bar =\n100” respectively, but omit the unit (μm). In contrast, Figure 5D correctly uses “Scale\nbar = 200 μm”, leading to inconsistent labeling that creates ambiguity.\nResponse: We appreciate the reviewer's attention to detail. We believe the\nrequested unit (µm) is already specified in the respective legends for Figure 2E\n(Page 73; Line 3-4) and Figure 6E (Page 77; Line 15), and we hope this clarifies\nthe point.\nWe ask you to include in the discussion that you did not explore potential effects of\nmethionine metabolism in other cell types and the potential effects of circulating\nmethionine - this should be acknowledged as a limitation.\nResponse: We thank the editor for raising this point. We fully agree that the\npotential effects of methionine metabolism in other cell types and the influence of\ncirculating methionine levels represent important considerations.\nWe have now acknowledged these limitations in the Discussion section with the\nfollowing statement: “…While we focused on macrophage-specific methionine\nmetabolism, we did not explore the potential contributions of other cell types\nwithin the atherosclerotic plaque, such as endothelial cells and vascular smooth\nmuscle cells. These cells likely compete for methionine metabolites, and their\ncomplex interactions could significantly influence the metabolic landscape.\nEqually important, our investigation was confined to the local plaque\nmicroenvironment and did not address the potential impact of systemic\nmethionine levels. Circulating methionine, influenced by dietary intake and\nhepatic metabolism, serves as the primary source for intraplaque methionine pools.\nFluctuations in systemic availability could modulate the metabolic\nreprogramming of all plaque cells, including macrophages, thereby influencing\natherosclerosis progression. Future studies incorporating cell-type-specific\nmetabolic analyses and investigating the links between circulating levels and\nintraplaque metabolism will be crucial to fully elucidate the role of this pathway\nin atherosclerosis…” (Page 31; Line 18-20 and Page 32; Line 1-11)\n4"}, {"round": 2, "reviewer_comments": {"reviewer_1": "Study: Methionine metabolism is important for pro-inflammatory phenotype of macrophages, which propagate\natherosclerosis. However, the role of methionine in atherosclerosis is not known. Methionine adenosyltransferase II alpha\n(MAT2A) is an important enzyme in methionine metabolism and highly expressed in atherosclerosis. MAT2A-deficient\nmacrophages have altered inflammation and migration. Mice with myeloid MAT2A deficiency have reduced plaque\nvulnerability. Epinephrine increases the mTOR-cMyc signaling, enhancing MAT2A expression.\nDuring the revision, the authors have added a substantial amount of additional data. Specifically, RNA sequencing in\natherosclerotic plaque macrophages, identification of epigenetic mechanisms, understanding the role of MAT2A in\nmacrophage recruitment in atherosclerotic plaques, and investigating the contribution of sympathetic activation have raised\nthe significance of the study.", "reviewer_2": "All my concerns were addressed by the authors. They performed additional experiments and included newly generated data\nto support their initial observations.", "reviewer_3": "The authors have fully addressed the previous comments with thorough revisions. However, there remain some minor\nformatting inconsistencies and oversights specifically in the files as outlined below:\n1. In multiple supplemental figures (e.g., Suppl Fig 1C/D, 2A/B), statistical significance is marked with symbols like “*” or “#”,\nbut the corresponding definitions of these symbols (e.g., “*p<0.05, **p<0.01”) are missing in the figure legends. This forces\nreaders to cross-reference the main text, reducing readability.\n2. In Suppl Tables 1, 4, 7, and 10, the P-value columns are misaligned with their corresponding variables (e.g., in Suppl\nTable 1, the P-value for “Male (n;%)” is offset from its row). This causes confusion about which variable each P-value\ncorresponds to.\n3. In Figure 2E and Figure 6E, the legends state “Scale bar = 200” and “Scale bar = 100” respectively, but omit the unit (μm).\nIn contrast, Figure 5D correctly uses “Scale bar = 200 μm”, leading to inconsistent labeling that creates ambiguity."}, "author_response": "RE: Manuscript Number: # NCOMMS-25-02859A\nMAT2A promotes atherosclerotic plaque vulnerability by mediating epigenetic\nreprogramming of macrophages\nTo the Editor and the Reviewers:\nWe extend our appreciation to the editor and reviewers for their constructive comments\non our manuscript. We have provided our point-by-point responses below and have\nrevised the manuscript files according to the checklist. The manuscript has been\nreformatted according to the journal's requirements, and we sincerely hope that the\nrevised version meets the standards for publication.\nReviewer #3 (Remarks to the Author):\n1. In multiple supplemental figures (e.g., Suppl Fig 1C/D, 2A/B), statistical significance\nis marked with symbols like “*” or “#”, but the corresponding definitions of these\nsymbols (e.g., “*p<0.05, **p<0.01”) are missing in the figure legends. This forces\nreaders to cross-reference the main text, reducing readability.\nResponse: We sincerely thank the reviewer for their meticulous attention to detail.\nInstead, the exact P-values (e.g., P = 0.0078) were directly displayed on the figures\nthemselves. Therefore, the issue of missing symbol definitions in the legends does\nnot apply to these particular figures, as the statistical information was already\nfully embedded within the figure panels. To better illustrate our findings, we have\nprepared the following figure which we believe provides clarity:\nSupplemental Figure 1. Patients and mice with advanced atherosclerotic plaques\nhave altered methionine metabolic profiles.\nmetabolomics from patients with TCFA-positive or TCFA-negative (C) (n=38 subjects)\nand ApoE-/- mice or controls (D) (n=5 individual animals) samples.\nSupplemental Figure 2. Increased MAT2A-mediated methionine metabolism in\nmonocytes/macrophages is correlated with atherosclerosis progression.\nThe mRNA levels analyzed by qRT-PCR in peripheral blood monocytes isolated from\npatients with TCFA-positive or TCFA-negative (A) (n=38 subjects) and ApoE-/- mice\nor controls (B) (n=5 individual animals) samples.\n2. In Suppl Tables 1, 4, 7, and 10, the P-value columns are misaligned with their\ncorresponding variables (e.g., in Suppl Table 1, the P-value for “Male (n;%)” is offset\nfrom its row). This causes confusion about which variable each P-value corresponds to.\nResponse: We meticulously re-examined all Supplementary Tables in the original\nsubmission. We found that the P-values and corresponding variables were\ncorrectly aligned according to standard formatting.\nThe final version of Supplemental Table 1, as it appears in the PDF, is presented\nbelow for clarity:\n2\n3. In Figure 2E and Figure 6E, the legends state “Scale bar = 200” and “Scale bar =\n100” respectively, but omit the unit (μm). In contrast, Figure 5D correctly uses “Scale\nbar = 200 μm”, leading to inconsistent labeling that creates ambiguity.\nResponse: We appreciate the reviewer's attention to detail. We believe the\nrequested unit (µm) is already specified in the respective legends for Figure 2E\n(Page 73; Line 3-4) and Figure 6E (Page 77; Line 15), and we hope this clarifies\nthe point.\nWe ask you to include in the discussion that you did not explore potential effects of\nmethionine metabolism in other cell types and the potential effects of circulating\nmethionine - this should be acknowledged as a limitation.\nResponse: We thank the editor for raising this point. We fully agree that the\npotential effects of methionine metabolism in other cell types and the influence of\ncirculating methionine levels represent important considerations.\nWe have now acknowledged these limitations in the Discussion section with the\nfollowing statement: “…While we focused on macrophage-specific methionine\nmetabolism, we did not explore the potential contributions of other cell types\nwithin the atherosclerotic plaque, such as endothelial cells and vascular smooth\nmuscle cells. These cells likely compete for methionine metabolites, and their\ncomplex interactions could significantly influence the metabolic landscape.\nEqually important, our investigation was confined to the local plaque\nmicroenvironment and did not address the potential impact of systemic\nmethionine levels. Circulating methionine, influenced by dietary intake and\nhepatic metabolism, serves as the primary source for intraplaque methionine pools.\nFluctuations in systemic availability could modulate the metabolic\nreprogramming of all plaque cells, including macrophages, thereby influencing\natherosclerosis progression. Future studies incorporating cell-type-specific\nmetabolic analyses and investigating the links between circulating levels and\nintraplaque metabolism will be crucial to fully elucidate the role of this pathway\nin atherosclerosis…” (Page 31; Line 18-20 and Page 32; Line 1-11)\n4"}]

Decision letter