{"paper_id":"492bf5fc-5396-4afc-a45a-7ceadf848c13","body_text":"Smad3 Regulates Smooth Muscle Cell Fate and Governs Adverse Remodeling and Calcification of Atherosclerotic Plaque | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Smad3 Regulates Smooth Muscle Cell Fate and Governs Adverse Remodeling and Calcification of Atherosclerotic Plaque Paul Cheng, Robert Wirka, Juyong Kim, Trieu Nguyen, Ramendra Kundu, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-708882/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Apr, 2022 Read the published version in Nature Cardiovascular Research → Version 1 posted You are reading this latest preprint version Abstract Atherosclerotic plaques consist mostly of smooth muscle cells (SMC), and genes that influence SMC biology can modulate coronary artery disease (CAD) risk. Allelic variation at 15q22.33 has been identified by genome-wide association studies to modify the risk of CAD, and is associated with expression of SMAD3 in SMC, but the mechanism by which this gene modifies CAD risk remains poorly understood. SMC-specific deletion of Smad3 in a murine atherosclerosis model resulted in greater plaque burden, more positive remodeling, and increased vascular calcification. Single-cell transcriptomic analyses revealed that loss of Smad3 altered SMC transition cell state toward two fates: a novel SMC phenotype that governs both vascular remodeling and recruitment of inflammatory cells, as well as a chondromyocyte fate. The remodeling population was marked by uniquely high Mmp3 and Cxcl12 expression, and its appearance correlated with higher risk plaque features such as increased positive remodeling and macrophage content. Further, investigation of transcriptional mechanisms by which Smad3 alters SMC cell fate revealed novel roles for Hox and Sox transcription factors whose direct interaction with Smad3 regulate an extensive transcriptional program balancing remodeling and vascular extracellular matrix with significant implications for atherosclerotic and Mendelian aortic aneurysmal diseases. Together, these data suggest that Smad3 expression in SMC inhibits the emergence of specific SMC phenotypic transition cells that mediate adverse plaque features, including positive remodeling, monocyte recruitment, and vascular calcification. Cardiac & Cardiovascular Systems atherosclerotic plaques Smad3 smooth muscle cells (SMC) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Decades of research and drug development have led to therapies and interventions that have significantly diminished morbidity and mortality from cardiovascular disease 1,2 . However, coronary artery disease (CAD) remains a leading cause of death in this country and worldwide with a sharp decline in the rate of improvement in mortality observed over the past decade 3 . Recent clinical studies targeting well-characterized risk factors such as lipids 4,5 and novel targets related to inflammation 6,7 have had modest results 8 suggesting a continued need to identify new disease modifiers. Over the past decade, genome wide association studies (GWAS) have identified over 160 loci that contribute to CAD risk 9,10 . Causal variation identified in these loci point primarily to genes and pathways predicted to function in the blood vessel wall to regulate disease risk 11–13 . Specific features of atherosclerotic plaque have been increasingly recognized to offer significant prognostic value. For instance, it has been noted that cellular composition, such as SMC contribution to the fibrous cap, influences risk of plaque rupture 14–16 . With advances in diagnostic imaging, novel features such as positive remodeling and microcalcification have been found to be highly predictive for myocardial infarctions 17,18 . Although counter-intuitive, studies using both intravascular ultrasound (IVUS), and longitudinal studies using CT coronary angiography 17,18 , have demonstrated that sites of outward remodeling are much more likely to be the culprit site for plaque rupture and myocardial infarction compared to sites with more luminal narrowing. However, little is known regarding the cellular and molecular mechanisms by which these high-risk features are controlled. A number of post-genomic GWAS follow-up studies in this and other laboratories investigating the genetic disease-related mechanisms of CAD have focused on SMC, and the relationship of SMC cell state changes to disease risk 19–23 . These studies have indicated that a significant portion of the CAD attributable risk is determined by this cell type 13 . Consistent with this notion, recent lineage tracing studies have demonstrated that the majority of cells inside atherosclerotic plaque, including those expressing some inflammatory markers, are oligo-clonal de-differentiated smooth muscle derivatives 24–27 . Genes that alter SMC behavior are known to influence the composition of atherosclerotic plaque 19,22,23,26,28 . CAD-associated genes TCF21 and AHR have been linked to these processes, and various genomic data suggests that additional CAD genes might also regulate SMC phenotype 21,28−32 . While these SMC progeny have been previously lumped together as phenotypically modulated SMC, advances in single cell RNA profiling has demonstrated the presence of subsets of these cells with distinct transcriptomes and cell fates. For example, medial SMC have been shown to give rise to fibroblast-like cells termed fibromyocytes 23 , as well as cells similar to endochondral bone forming cells, chondromyocytes 19,30 , among other bioinformatically defined populations 19,33 . However, the functional significance and relative location of these transcriptionally distinct populations remain to be elucidated. The TGFβ signaling pathway is central to smooth muscle biology during development and disease, and an important modifier of atherosclerosis 34,35 . Canonical TGFβ signaling is thought to be mediated through Smad family proteins, particularly nuclear signaling factors Smad2 and Smad3. While themselves poor binders to DNA 36 , through their interaction with other transcription factors, the Smad factors are central to key transcriptional programs that regulate cell fate in development and disease 37,38 . Multiple GWAS have identified rs17293632 9 , a single nucleotide variant that lies within a functional smooth muscle enhancer that regulates SMAD3 expression 20 , as an important modifier of risk for myocardial infarction. However, how smooth muscle SMAD3 expression influences risk of myocardial infarction is unclear. In vitro , SMAD3 appears to modify smooth muscle cell differentiation and proliferation through its interaction with other transcription factors critical to SMC biology and risk of CAD 29 . However, the exact effects of Smad3 expression level on SMC plaque biology remain unknown. Here, we demonstrate that SMC-specific deletion of Smad3 influences the fate of de-differentiated SMC in atherosclerotic plaques in vivo , promoting both a new pro-remodeling SMC transition phenotype that expresses remodeling genes such as Mmp3 and inflammatory chemokines such as Cxcl12, as well as an expansion of the SMC-derived chondromyocyte (CMC) population. These cellular changes are associated with increased positive remodeling and plaque calcification that appear to be directed by Smad3 in conjunction with transcriptional effects of Hox and Sox factors. Results SMC-specific deletion of Smad3 is associated with increased lesion burden, outward remodeling, and increased numbers of both SMC progeny and monocyte/macrophage lineage cells To understand how smooth muscle expression of Smad3 influences atherosclerotic lesions in vivo , we generated a murine model of atherosclerosis with established smooth-muscle specific Cre ( Myh11-Cre ) crossed with a conditional knockout allele of Smad3 39–41 with concurrent lineage tracing provided by conditional tandem dimer tomato ( ROSA Tdt ) expression on the ApoE null background ( Smad3 ΔSMC ) (Fig. 1 A). To limit confounding created by the critical role of Smad3 during development, the Smad3 gene was deleted via a tamoxifen inducible Cre only after mice had reached maturity (8-weeks-old), immediately prior to initiation of a Western high-fat diet (HFD, Fig. 1 A). The Smad3 conditional knockout mice grew to maturity with no significant change in weight or mortality compared to control (Suppl. Figure 1A, B), with highly efficient Cre-mediated deletion of the floxed DNA binding domain of Smad3 (Suppl Fig. 1C). Examination of atherosclerotic lesions in the aortic root after 16 weeks of HFD demonstrated that SMC from Smad3 ΔSMC mice were able to migrate into the lesion, expand, and contribute to the formation of atherosclerotic plaque and the fibrous cap (Figs. 1 B, 1 C). Quantification of atherosclerotic lesions revealed a significant increase in plaque volume in Smad3 ΔSMC compared to control animals (Fig. 1 D). To further characterize the anatomy of diseased vessels, specifically regarding outward remodeling vs luminal narrowing, we evaluated the area encapsulated by the diseased vessel as well as lumen area. The lumen area in these sections showed no significant change (Fig. 1 E), but the area circumscribed by the external elastic lamina was significantly increased (Fig. 1 F). These findings are consistent with expansion of atherosclerotic plaque volume in conjunction with outward “positive” remodeling. To determine the cellular anatomy associated with the increased plaque size, we quantified the area occupied by fluorescent tdTomato SMC lineage traced cells (Fig. 1 G, 1 H) as well as CD68 stained monocytes and macrophages in the lesions (Fig. 1 I, 1 J). This analysis revealed a statistically significant increase in area for both SMC-derived cells as well as cells of the monocyte-macrophage lineage. Given that the lineage tracing Myh11-Cre transgene is active only in cells emanating from mature SMC, these findings suggest that the increased lesion growth has both a cell autonomous as well as a non-autonomous cellular component, with the latter reflecting an SMC mediated effect on monocyte-macrophage lesion recruitment. Single cell transcriptomic profiling reveals Smad3 deletion alters SMC fate to promote a pro-remodeling and chondromyocyte phenotype Given the critical role that Smad3 plays in cell fate decisions during development, we hypothesized that alteration in disease-associated SMC phenotype transitions might account for the observed cellular lesion characteristics as well as recruitment of CD68 + cells and positive remodeling. Thus, to better understand how loss of Smad3 expression produced phenotypic changes in lesion SMC derived cells, and their interactions with other lesion cell types, we performed single cell RNA expression profiling (scRNAseq) of atherosclerotic lesions from Smad3 knockout ( Smad3 ΔSMC ) and control animals. The atherosclerotic tissue was harvested, processed for single cell encapsulation, RNA capture, reverse transcription and amplification with the 10X Genomics Chromium V3 platform, and cDNA libraries were sequenced as previously described 23,28 . Subsequently, the single cell expression data were visualized utilizing Uniform Manifold Approximation and Projection (UMAP) to create a 2-D projection representing the organization of individual cells to each other in clusters, and of the relationship of the organized clusters to each other. After filtering and normalization, a total of 26,219 cells from control and 35,518 cells from Smad3 ΔSMC mice were included in the analysis obtained from 3 and 4 independent captures of pooled tissue from 2 mice in each individual capture. We first investigated clustering of the scRNAseq data using standard settings for the principal component analysis and number of clusters in UMAP space. Feature plots were employed to visualize the SMC lineage traced cells (Fig. 2 A). SMC traced cells were identified by expression of the tdTomato gene, and the component of this cluster representing mature medial SMC was identified by expression of SMC lineage markers Myh11 and Cnn1 . As we have shown previously, a significant portion of the SMC-lineage traced cells during disease did not express these mature SMC markers and thus represented SMC that had undergone phenotypic transition 22,23,28 . We were thus able to determine whether there were alterations in the number of mature differentiated versus transition SMC in Smad3 ΔSMC mice. We investigated whether there was an alteration in the number of differentiated SMC among the lineage traced (tdTomato+) cells, defining “differentiated” cells using classical Myh11 or Cnn1 expression, along with unbiased clustering (SMC vs transition SMC) (Fig. 2 A, 2 B). Cells were clustered based on the scRNAseq data using the Lovain algorithm to a resolution where the SMC derived cells are split into two distinct clusters, as we and other have characterized 23 . Regardless of the definition of “mature SMC”, there was a significant decrease in the proportion of mature SMC in the Smad3 ΔSMC compared to control mice (Fig. 2 A, 2 B). To discern the optimum biologically relevant resolution of clustering and avoid over-fitting with the Lovain algorithm, the clustering resolution was empirically determined as the minimal settings that allowed for separation of known biologically distinct populations of endothelial cells (EC) (Figs. 2 C, 2 D), i.e., blood vessel EC (VE-cadherin expressing, Prox1 negative) vs lymphatic EC (Prox1 expressing) endothelial cells 42,43 . The identities of the cellular subpopulations that were created with this approach were then determined based on specifically expressed genes in each cellular cluster with the monocyte-macrophage lineage identified with CD68 expression, and the previously described SMC transition fibromyocytes by the marker Tnfrsf11b (Fig. 2 C). The validity of this clustering approach was further confirmed via visualization of cell population canonical “markers” for all clusters in the UMAP space. Previous work in SMC lineage tracing in atherosclerosis has identified three distinct clusters of SMC derived cells, including mature SMC, fibromyocytes (FMC), and pro-calcific chondromyocytes (CMC) 19,28,33,44 . Similar subsets of lineage traced SMC were identified in these data (Fig. 2 D), and in addition two distinct new clusters of Myh11-Cre lineage labeled populations were also identified. One cluster was composed of pericytes, and an additional small population of novel disease-associated SMC-derived cells clustered by itself as a unique transcriptomic phenotype, that we named “remodeling-SMC” (R-SMC), to be studied in more detail below. To determine if loss of Smad3 expression alters the cell fate transitions of disease-associated SMC lineage cells, we ascertained the fraction of de-differentiated cells that contribute to clusters for each of the two known and the novel transition phenotypes. Smad3 ΔSMC transition SMC showed an increase in proportion of CMC along with the newly defined population at the expense of the more differentiated FMC (Fig. 2 E, F). The increase in fraction of CMC among disease-associated Smad3 ΔSMC cells corresponded with a significant increase in lesion area expressing chondromyocyte marker Col2a1 (Fig. 2 G, H). Functionally, this increase in Col2a1 expressing cells correlated with an increase in lesion calcified area as assayed by von Kossa staining of atherosclerotic lesions (Fig. 2 I, J). These cells appeared to result from an increase in proliferation of de-differentiated SMC, as evidenced by a higher level and increased proportion of cells expressing proliferation markers such as Mki67, Ccnd1, Ccnb1 , and Myc , with limited alteration in cell-cycle inhibitor genes Cdkn2a/b (Fig. 2 K). To further assess this possibility, we generated a “chondrocyte proliferation score” that represents a scaled-average expression of all genes associated with GO category “promote chondrocyte proliferation” with each individual cells’ transcriptome 45 . Consistent with higher expression of specific cell-cycle regulators, Smad3 ΔSMC transition SMC had a significantly higher chondrocyte proliferation score than control cells (Fig. 2 L). Applying the analysis utilizing a “mesenchymal proliferation score” based on GO categories of “promote mesenchymal proliferation” produced equally significant results (Sup. Figure 2), further suggesting that the increase in number of tdTomato positive cells in the lesions likely reflects proliferation of SMC derivative cells. The increase in cell number did not result from alterations in apoptosis, since there was no difference in TUNEL staining of control vs Smad3 ΔSMC sections (Sup. Fig, 3). A novel pro-remodeling SMC phenotype is characterized by expression of Mmp3 and leukocyte recruiting factors In addition to increased CMC in the Smad3 ΔSMC animals, there was also an increase in the proportion of transition SMC that contributed to the newly identified cell cluster (Figs. 2 D, 3 A), that we refer to as remodeling-SMC (R-SMC) due to their high expression of genes involved in extracellular matrix remodeling. These cells were identified at the junction of CMC, fibroblast and FMC clusters. The vast majority of cells within this cluster were lineage traced at a proportion similar to that seen with CMC and FMC (Fig. 3 B) confirming they were SMC derived. In vascular lesions, they constituted 6% of SMC transition cells in Smad3 ΔSMC . While their gene expression pattern is most similar to that identified in CMC as shown by their juxtaposition in UMAP space and by hierarchical clustering, they were easily distinguished from CMC at the transcriptional level (Fig. 3 D). To investigate the ontogenic relationship of this group of cells to SMC, FMC, and CMC, we ported the scRNAseq data to Slingshot 46 , a lineage inference tool designed to map trajectories involving multiple branching lineages. Applying this algorithm suggested that SMC give rise to FMC which in turn serve as a source for both CMC and R-SMC (Fig. 3 C). Interestingly, the R-SMC cell population was marked by a particularly high expression of matrix metalloproteinase-3 ( Mmp3 ) (Figs. 3 E, 3 F), an enzyme required for outward remodeling of atherosclerotic vessels 47 . Mmp3 was in fact among the most upregulated genes in this population compared to other SMC transition groups. Given our observation of increased outward remodeling identified in Smad3 ΔSMC mice, this finding was further investigated. Single-cell transcriptomic data suggested a higher number of Mmp3 -expressing cells and an overall higher level of Mmp3 expression in Smad3 ΔSMC than control cells (Sup. Figure 4A, 4C). In addition to increased mRNA levels, an in vitro functional study utilizing a florescent based Mmp3-activity assay demonstrated that homogenized aortic tissue from Smad3 ΔSMC mice has more Mmp3 activity than control tissue (Sup Fig. 4B). In situ hybridization with RNAscope showed Mmp3 -expressing R-SMC to be a distinct population from CMC, which were marked by Col2a1 expression (Fig. 3 G), indicating that these R-SMC transition cells are juxtaposed but not overlapping in the lesion plaque and further supporting their distinct phenotype. Consistent with the established role for MMP3 in positive remodeling, its expression was most prominent at the base of the atherosclerotic lesion in cells juxtaposed to the elastic lamina. Strikingly, these MMP3 cells were commonly associated with areas of disrupted elastic lamina, consistent with their possible invasion through this structure (Fig. 3 F, 3 G, Sup Fig. 4D). Importantly, the same rare population of MMP3 expressing cells were also found in human coronary arteries, where they were also associated with regions of disrupted elastic lamina (Fig. 3 H). There were more cells expressing Mmp3 in Smad3 ΔSMC compared to controls as per the scRNAseq data and more prominent staining in Smad3 ΔSMC vs control (Fig. 3 F, Sup Fig. 4A). Mmp3 expression was altered only in the SMC derived cells, and not significantly different in the non-SMC derived population, suggesting that altered Mmp3 levels by Smad3 happens in a cell-autonomous manner in SMC lineage transition cells (Sup Fig. 4A). By performing TGF-β stimulation of human coronary artery smooth muscle cells, we were able to show that MMP3 is suppressed by TGF-β (Fig. 3 I). SMAD3 knockdown did not significantly increase the expression of MMP3 at baseline, but did negate the TGF-β dependent suppression of expression, suggesting SMAD3 is required for the TGF-β dependent regulation of MMP3 in atherosclerotic lesions. To better understand the vascular function of this cell population, we performed pathway analyses of all significantly differentially expressed genes in the R-SMC as compared to other de-differentiated SMC phenotypes (Sup Table 1). PANTHER analysis revealed the top biological processes enriched in this list of genes are related to remodeling of ECM followed by regulation of chemotaxis and inflammation (Fig. 3 J). Analysis of genes contributing to the enrichment of these pathways reveal that this population of cells express higher levels of multiple chemoattractants whose receptors are primarily restricted to macrophages (Fig. 3 K). For example, this population is the main SMC source of Cxcl12 in the lesion, with the Cxcr4 receptor for this ligand being expressed exclusively by lesion macrophages. In addition, other R-SMC differentially expressed cytokine genes, including Saa3 , and Ccl2 , all have well-established roles in monocyte recruitment, with their respective receptor genes Tlr1 and Ccr2 expressed mostly by inflammatory cells in the lesion (Fig. 3 K). There is high overlap between the Mmp3 high cells and cytokine expressing cells. In fact, if a marker-based definition of a pro-remodeling and recruitment population were defined as cells with elevation of Mmp3 and Cxcl12 , one can re-capitulate the unbiased clustering results (Sup Fig. 4E). Consistent with the hypothesis that SMAD3 regulates the SMC chemotaxis signal to inflammatory cells, in vitro transwell migration studies demonstrated augmentation of HCASMC ability to recruit human monocytes (Sup Fig. 4F). Taken together, these findings suggest that beyond its role in positive remodeling, this novel population of cells may orchestrate monocyte recruitment and underlie the increase in CD68 positive cells in lesions. Given the increased monocyte-macrophage recruitment in Smad3 ΔSMC plaques, we investigated the cellular phenotypes and gene expression patterns in these cells. Three of these clusters were distinguished by expression of Cd168 , Spp1 , and Ccr1 , and a fourth cluster with a general macrophage lineage pattern of gene expression ( Sup Fig. 5). Surprisingly, the relative contribution of each of these clusters to the overall makeup of the macrophage lineage was not changed by loss of Smad3 expression. While there were increased numbers of monocyte-macrophage cells, there was not a significant difference in the relative makeup of the lesion macrophages by these specific subgroups, or difference in overall expression of inflammatory mediators. Smad3 cooperates with other transcription factors to regulate SMC genes that are involved in human vascular remodeling and disease Given that Smad3 is likely affecting all SMC lineage phenotypes beyond just the CMC and R-SMC populations described above, we characterized the alteration in gene expression of all de-differentiated SMC derived cells in the atherosclerotic lesions. To rigorously identify differentially regulated genes, we employed a Wilcoxon rank sum-based analysis comparing Smad3 ΔSMC versus control mouse data for FMC plus CMC and R-SMC cellular clusters and 83 genes were identified (Sup. Table 2). Pathway analysis performed using DAVID demonstrated significant enrichment in TGFβ signaling, which is consistent with Smad3’s role in the TGFβ pathway (Fig. 4 A). In addition, biological processes broadly related to ECM remodeling, SMC differentiation, and chemotaxis were also highly enriched. Utilizing GREAT 48 , we compared the list of 83 identified Smad3 ΔSMC marker genes to the top 1000 genes expressed by de-differentiated SMC in diseased murine vessels. The analysis revealed enrichment for genes linked to human atherosclerosis and arterial dilatation (Fig. 4 B). In addition to Mmp3 , and consistent with the observed phenotype of increased outward remodeling in Smad3 ΔSMC mice, Lox and Mfap5 were also among the top significantly down-regulated genes in de-differentiated Smad3 ΔSMC SMC (Sup table 2). Pathogenic loss of function in both of these genes has been linked to human vascular syndromes including aortic aneurysms 49−51 . To investigate whether these genes are directly regulated by TGFβ, we stimulated human coronary artery smooth muscle cells (HCASMC) with TGFβ in the presence and absence of SMAD3 . A subset of genes, such as LOX , appeared to be directly regulated by TGFβ in HCASMC, and this effect was abrogated by SMAD3 silencing (Fig. 4 C), similar to the regulation of MMP3 . However, a subset of genes, such as MFAP5 , did not appear to be TGFβ responsive, but were sensitive to loss of SMAD3 (Fig. 4 C). These findings suggest involvement of additional co-regulatory factors. To better understand the context in which Smad3 regulates this complex transcriptional program and identify possible co-regulatory factors, we analyzed the 5’ regulatory elements of the 83 Smad3 differentially regulated genes to look for enriched transcription factor motifs. Motif analysis conducted with HOMER revealed enrichment for Hox/homeobox motifs as well as Sox9/10 related motifs (Fig. 4 D). Hox and Sox genes were not down-regulated in the Smad3 ΔSMC SMC (Sup. Figure 6A), suggesting that these factors are likely directly interacting with Smad3 to regulate a joint transcriptional program. Consistent with this hypothesis, Sox9 is known to be a critical transcription factor regulating calcification, and has been shown in chondrosarcoma cells to selectively interact with Smad3, but not Smad2, to modulate an endochondral ossification program 52 . Since Hoxb2 and Sox9 were found to be coordinately expressed in transition SMC (Suppl. Figure 6B) we further investigated the possible interaction of these factors with Smad3. Knocking down SOX9 and HOXB2 , the most highly expressed HOX gene in human coronary SMC, recapitulated the changes in expression of key vascular remodeling genes MMP3 and MFAP5 observed in vivo in mouse (Fig. 4 E). Thus, we hypothesize that HOX factors, whose motifs are also enriched, can directly interact with SMAD3. To test this hypothesis, we performed nuclear co-immunoprecipitation (co-IP) experiments to test their interaction. In cells over-expressing His-HOXB2 or Flag-SOX9, SMAD3 protein was co-immunoprecipitated with anti- His or Flag antibody respectively (Fig. 4 F, Sup Fig. 7). Furthermore, proximity ligation assays demonstrated that endogenous SMAD3 is localized in proximity (< 40nm) to HOXB2 and SOX9 to suggest their involvement in multi-protein complexes in the nucleus of HCASMC (Fig. 4 G). Taken together, these data suggest HOX family proteins such as HOXB2, as well as SOX family member SOX9, physically interact in the nucleus to regulate transcription of targeted genes. To test the functional significance and epistatic relationship of these findings, we cloned an evolutionarily conserved regulatory region near the 5’ end of the human MFAP5 gene, which contains conserved putative HOX, SOX, and SMAD binding elements, into a luciferase vector and tested its ability to respond to SOX9 and HOXB2 binding. SOX9 and HOXB2 efficiently activated this enhancer element but not the control luciferase vector, further establishing a role in this transcriptional program (Fig. 4 H, Sup Fig. 6C). Knocking down SMAD3 diminished the SOX9 and HOXB2-dependent activation of the luciferase construct, suggesting interaction with SMAD3 is required for regulation of MFAP5 (Fig. 4 H). Discussion In these experiments we have studied Smad3 ΔSMC mice with scRNAseq, cellular anatomy, and lesion in situ studies. Highly correlated results from these different approaches provide a consistent picture of the role of this CAD associated transcription factor in vascular disease processes. Applying scRNAseq at unprecedented read depth and cell number combined with lineage tracing allowed us to observe changes in the number of SMC transition cells and transcriptomic changes that were previously unobtainable. These data identified cell state changes related to loss of Smad3 expression, such as increased SMC transition to the CMC phenotype, a finding substantiated with increased Col2a1 plaque cell expression and vascular calcification. Furthermore, the loss of Smad3 promoted SMC transition to a unique SMC phenotype cell, R-SMC, which have an apparent role in modulating vascular remodeling and macrophage recruitment. Increased expression of cellular proliferation genes by scRNAseq was consistent with the increased number of SMC lineage traced cells in the plaque (Fig. 5 ), echoing previous in vitro findings in HCASMC 29 . Finally, leveraging rich transcriptomic data from a large number of cells, we identified transcriptional interaction of SMAD3 with HOX and SOX factors in the regulation of genes expressed in de-differentiated SMC transition phenotypes. The high resolution scRNAseq data has allowed us to describe a small but distinct subset of SMC transition cells that expresses pro-remodeling enzymes. Mmp3, a metalloprotease required for positive remodeling, is expressed most prominently in the R-SMC that are located in the basal plaque where they appear to migrate into the media towards the adventitia, potentially related to the previously described SMC lineage traced population in the adventitia 53 . Strikingly, in human coronary arteries, this MMP3 expressing pro-remodeling population resides in the same regions of the artery with observed disruption of the nearby elastic lamina, consistent with a role promoting positive remodeling in human coronary disease. These findings suggest that expansion of this population may be a significant contributing factor to positive remodeling and other adverse features. The importance of this MMP3-expressing SMC transition population in modifying plaque rupture risk may explain the seemingly paradoxical observation that a SNP associated with lower-expression of MMP3 is associated with greater luminal coronary artery stenosis on cardiac catheterization, but the higher-expressing variant is associated with more myocardial infarctions 54 . These genetic observations provide further evidence that modulation of R-SMC alters plaque features and CAD risk in human. While Mmp3 was previously thought to be expressed by macrophages in the lesions 54 , we found no evidence that Mmp3 was expressed by cells in the macrophage cluster. Previous work has shown that IL1 drives positive remodeling of plaques and this function was completely reversed by deletion of Mmp3 47 . This suggests that SMC are the primary mechanism for the high-risk plaque feature of positive remodeling, as seen in the Smad3 ΔSMC mice, and may account in part for the beneficial effect of IL1 blockade on the risk of plaque rupture in humans 6 . Mmp3 also appears differentially regulated among restricted populations of SMC progeny in carotid artery plaque 19 , suggesting R-SMC exist in other atherosclerotic beds. Interestingly, this population of R-SMC also expresses a number of chemokines, including Cxcl12 , whose main receptor Cxcr4 is expressed solely on the monocyte-macrophage lineage. This suggests that the R-SMC population likely plays a role in regulating the inflammatory response to the lesion, contributing to the increase in observed monocyte-macrophage population detected in the lesions. The combination of remodeling and inflammatory cell recruitment, both factors that determine plaque stability, highlights the critical role that this specific sup-population of cells may play in modulating human disease risk. This contributes to existing literature 55 suggesting that SMC play a central role in regulating inflammatory cell recruitment and retention in atherosclerotic plaque and identifies a specific sub-population of transition SMC critical for high-risk plaque features. There was also an increase in CMC in Smad3 ΔSMC mice suggesting Smad3 actively inhibits differentiation to this phenotype or inhibits their proliferation. The transcriptomic, topological, and lineage inference data presented here indicate that they are a distinct population from Mmp3 -expressing R-SMC. The CMC exhibit a chondrogenic transcriptomic program 28 , expressing Col2a1 , Acan , and Sox9 , with similarities to chondrogenic progenitors in endochondral bone formation and repair. Smad3 has been shown to regulate Sox factor transcriptional activity in a TGFβ-independent manner through physical interactions 52,56 . The observed expansion of the CMC also provides an interesting parallel to established findings of accelerated bone and wound healing in Smad3 knockout mice 57,58 . The concomitant increase in Col2a1 expressing cells in the plaque and increased vascular calcification suggests that this cell type is at least partially responsible for coronary calcifications seen in human coronary artery disease. It remains to be determined whether increased calcification is harmful or protective in terms of plaque rupture risk, since conflicting observational data exists in humans. While increased coronary calcification is correlated with increased risk of myocardial infarction 59 , local calcification appears to be protective against plaque rupture 60−62 and interventions that lower risk of plaque rupture increase calcification 63,64 . Given the multiple populations of SMC-derived transition cells observed in our studies, their relative ratios could possibly determine the quality of calcification as well, which is also considered to confer differential risk of plaque stability 65,66 . Recent work by Chen et al. 44 has employed single cell studies to investigate the role of Tgfβ signaling in vascular disease, employing a combined Marfan II/ Loeys-Dietz and hypercholesterolemia mouse model. A key finding by these investigators was evidence for an SMC derived mesenchymal stem cell that gives rise to numerous cell types, including adipocytes, osteoblasts/chondrocytes (CMC) and macrophages, in the context of Tgfrb2 knockout and high fat diet. In their study increased plaque inflammation was due in large part to increased SMC-derived macrophage number in the vessel wall through this process. In studies reported here, we did not find evidence for an SMC-derived stem cell that mediates this effect and no evidence that SMC can transition into adipocytes or macrophages in wildtype or Smad3 KO mice. By contrast, we found that wildtype SMC give rise to fibroblast-like (fibromyocyte) phenotype cells that subsequently give rise to CMC, and with Smad3 KO the unique cluster of R-SMC derived cells. Interestingly, other published scRNAseq studies have found evidence both for and against SMC-macrophage transition 19,23,33,44,67 . The observed differences between the Tgfbr2 KO and Smad3 KO suggest a fundamentally different mechanism by which SMC transition in response to signaling through these two different molecules. Numerous other differences are identified with comparison of the Smad3 and Tgfbr2 knockout models. We do not find evidence for the dramatic increase in lipid accumulation in the Smad3 mice that was seen in the Tgfbr2 mice. Interestingly, we also did not find increased medial thickness, and the calcification that we identified in the Smad3 KO mice was in the plaque, not in the media as described for Tgfbr2 . This is an important difference, given the relationship of different types of calcification to disease risk, and consistent with differences in the relative number of CMC generated in each disease model. Our studies showed an increased diameter of the diseased aortic tissue, but without differential loss of medial SMC and no evidence of aneurysm formation as identified in the Tgfbr2 KO model. Regarding MMP genes that likely have a role in aneurysm and remodeling, both Mmp2 and Mmp3 were highly up-regulated in the Tgfbr2 KO but only Mmp3 was upregulated in the Smad3 KO. Overall, gene ontology analysis with differentially regulated genes in the Smad3 KO identified primarily atherosclerosis and aneurysm terms while this analysis with Tgfbr2 KO mice identified terms related primarily to vascular calcification. These differences between two TGFB pathway molecules are consistent with the known complexity of TGRBR2 and SMAD3 signaling. While SMAD3 was originally identified and characterized in the context of canonical TGFB signaling 68 , it was also shown that TGFB receptors can signal through SMAD2 as well as other non-SMAD “non-canonical” signaling pathways, including those mediated by mitogen activated protein (MAP) kinases (ERK, p38 and JNK), phosphatidylinositol-3-kinase (PI3K) and RHO-like GTPases in different cell types 69 . SMAD3 also binds and is regulated by ancillary pathway factors such as FHL3, SKI and ZEB2, with each of these factors in turn regulated by a variety of pathways that can signal independently of TGFB. These fundamental differences between TGFBR2 and SMAD3 signaling are exemplified by their different embryonic phenotypes, with Tgfbr2 knockout embryos undergoing fatality at 10.5 days post conception while Smad3 knockout mice are born and survive into adulthood. Given these differences in molecular signaling, developmental loss-of-function phenotypes, and striking differences in cellular and molecular single cell analyses in the context of atherosclerosis, we surmise that SMAD3 and TGFBR2 have overlapping but distinct signaling mechanisms, with differential disease related effects on SMC transition phenotypes. Beyond the changes in proportions of the different SMC derivatives, loss of Smad3 also resulted in alterations in SMC transition phenotype transcriptomes as a whole. Gene knockout down-regulated several important ECM genes, including Lox, Mfap5 and Eln , whose loss of function mutations have been associated with Mendelian aortopathies. These findings suggest that global transcriptomic changes associated with Smad3 loss weaken the vascular wall and thus further promote positive or outward vascular remodeling. These finding may also have implications in non-atherosclerotic vasculopathies, such as Marfan and Loeys-Dietz syndromes. Aortopathies such as Marfan’s syndrome have been shown to produce aberrant SMC derived populations that contribute to pathogenesis 70 . In fact, our previous scRNAseq studies of a murine Marfan model also demonstrated an increase in Mmp3 expressing SMC progeny and lower Mfap5 expression, suggesting our findings here may extend beyond atherosclerotic disease. Human genetics data has previously suggested the lead CAD-associated SNP rs56062135 at 15q22 is in linkage disequilibrium (LD) with SNP rs17293632 that appears to promote AP-1 binding and increase SMAD3 expression in vitro and in vivo 20 , suggesting higher SMAD3 expression may be associated with risk of myocardial infarction 9,20,29 . This is clearly contradictory to our finding that complete loss of Smad3 increases plaque size in our murine atherosclerosis model, and there are several possible explanations for this disparity. It is possible that alternate SNPs in LD with rs56062135 have the opposite effect on SMAD3 expression in the context of certain types of cellular stimulation, i.e., serve as response QTLs. In this case, the response QTLs may have a greater effect on SMAD3 expression and the integrative effect of the entire haploblock on SMAD3 expression would be opposite and greater than the effect of rs17294632. Alternatively, it is possible that cell-fate changes identified in Smad3 ΔSMC mice actually overall stabilize the human lesion and thereby protect it from plaque rupture, despite there being larger lesion size and plaque burden. This paradoxical effect has previously been observed. For example, the IL1β blocking antibody canakinumab decreased the risk of myocardial infarction in human trials but Il1r blockade/knockout increased the lumen obstruction and plaque size in mouse models 47 71 72 . Importantly, Il1r1 loss drastically changed plaque composition, suggesting SMC cell fate in plaques may be a stronger determinant for plaque rupture than plaque size alone. Indeed, it has been observed that the largest plaques seen on coronary angiogram are usually not the ones that rupture and cause myocardial infarction 73 . Recent human epidemiological and clinical data also suggest that the quality of calcification is critical, and that some types of more calcified plaques are less likely to rupture 66 . It is possible that the increased calcification seen in Smad3 ΔSMC mice is correlated with lesions in humans that are protected against myocardial infarction, which then contributes to the protective genetic signal. The exact explanation for these seemingly opposite findings holds the key to translating human genetics into better understanding of the pathophysiology and identifying new molecular therapies for atherosclerosis, and will only be discovered by detailed mechanistic studies of additional genetic loci that harbor risk for coronary artery disease. Materials And Methods Mouse strains SMC-specific lineage tracing and Smad3 knockout was generated by a well-characterized BAC transgene that expresses a tamoxifen-inducible Cre recombinase driven by the SMC-specific Myh11 promoter ( Tg Myh11−CreERT2 ; 019079; JAX). These mice were bred with a floxed-stop-flox tdTomato fluorescent reporter line (B6.Cg- Gt(ROSA)26Sor tm14(CAGtdTomato)Hze /J; 007914; JAX) to allow SMC-specific lineage tracing. Smad3 conditional knockout were obtained from Matzuk lab from Univ Texas SW 39,40 with LoxP sites flanking exons 2 and 3 which contains Smad3 DNA binding domain and creates a non-functioning frame-shift mutation after deletion 41 . All mice were back-crossed onto the C56BL/6 ApoE −/− background. As the Cre-expressing BAC was integrated into the Y chromosome, all lineage-tracing mice in the study were male. The animal study protocol was approved by the Administrative Panel on Laboratory Animal Care at Stanford University. Induction of lineage marker and Smad3 knockout by Cre recombinase For all experiments, tamoxifen gavage schedule was as follows: two doses of tamoxifen, at 0.2 mg g − 1 bodyweight, were administered by oral gavage at 7–8 weeks of age, with each dose separated by 72–96 hrs. HFD was started (101511; Dyets; 21% anhydrous milk fat, 19% casein and 0.15% cholesterol) after the second gavage. Mouse aortic root/ascending aorta cell dissociation Immediately after sacrifice, mice were perfused with phosphate buffered saline (PBS). The aortic root and ascending aorta were excised, up to the level of the brachiocephalic artery. Tissue was washed three times in PBS, placed into an enzymatic dissociation cocktail (2 U ml − 1 Liberase (5401127001; Sigma–Aldrich) and 2 U ml − 1 elastase (LS002279; Worthington) in Hank’s Balanced Salt Solution (HBSS)) and minced. After incubation at 37°C for 1 h, the cell suspension was strained and then pelleted by centrifugation at 500 g for 5 min. The enzyme solution was then discarded, and cells were resuspended in fresh HBSS. To increase biological replication, multiple mice were used to obtain single-cell suspensions at each time point. For each scRNA capture, 2 mice were used. 4 separate pairs of isolation were performed for control and Smad3 ΔSMC , but one control 10X capture unexpectedly failed resulting in a final of 3 captures of control and 4 captures from conditional KO that was included in the analysis. Cells were sorted FACS sorted based off tdTomato expression. tdT + cells (considered to be of SMC lineage) and tdT − cells were then captured on separate but parallel runs of the same scRNA-Seq workflow (gating strategy and threshold identical to those published in previous work by Wirka et al 24 ), and datasets were later combined for all subsequent analyses. Single-cell capture and library preparation and sequencing All single-cell capture and library preparation was performed at the Stanford Functional Genomics Facility and Stanford Genomic Sequencing Service Center. Cells were loaded into a 10x Genomics microfluidics chip and encapsulated with barcoded oligo-dT-containing gel beads using the 10x Genomics Chromium controller according to the manufacturer’s instructions. Single-cell libraries were then constructed according to the manufacturer’s instructions (Illumina). Libraries from individual samples were multiplexed into one lane before sequencing on an Illumina platforms with targeted depth of 50,000 reads per cell. Human coronary artery cell section Human coronary arteries used in this study were dissected from explanted hearts of transplant recipients, and were obtained from the Human Biorepository Tissue Research Bank under the Department of Cardiothoracic Surgery from consenting patients with approval from the Stanford University Institutional Review Board as previously described. Preparation of mouse aortic root sections Immediately after sacrifice, mice were perfused with 0.4% paraformaldehyde (PFA). The mouse aortic root and proximal ascending aorta, along with the base of the heart, was excised and immersed in 4% PFA at 4°C for 24 hrs. After passing through a sucrose gradient, tissue was frozen in optimal cutting temperature compound (OCT) to make blocks. Blocks were cut into 7-µm-thick sections for further analysis. Immunohistochemistry IHC was performed according to standard protocol. Primary antibodies: Anti-SM22alpha rabbit polyclonal primary antibody (1:300 dilution; ab14106; Abcam), a Mmp3 Rabbit monoclonal antibody (1:200 dilution; Abcam 52915 ) or a CD68 rabbit polyclonal antibody (1:400 dilution; ab125212; Abcam). Secondary: Rabbit-on-Rodent HRP Polymer (RMR622; Biocare Medical). The processed sections were visualized using a Leica DM5500 microscope objective magnifications, and images were obtained using Leica Application Suite X software. Sections obtained at equal distance measured from the superior margin of the aortic sinus were used for comparison. Areas of interest were quantified using ImageJ (National Institutes of Health) software, and compared using a two-sided t -test. Lesion size was defined by the area encompassing the intimal edge of the lesion to the border of Tagln positive intima-media junction. Area encompassed by the vessel media was defined by area encircled by the outer edge of Tagln staining of vessel media. All area quantification was performed in a genotype blinded fashion with image J using length information embedded in exported files. Von Kossa stain was performed using Abcam 150687 kit with manufacturer’s recommended protocol with 90-minute development time. All biological replicates for each staining were performed simultaneously on position-matched aortic root sections to limit intra-experimental variance. Folded sections that were uninterpretable after processing were removed. RNAscope assay Slides were processed according to the manufacturer’s instructions, and all reagents were obtained from ACD Bio. In short, slides were washed once in PBS, then immersed in 1× Target Retrieval reagent at 100°C for 5 min. Slides were washed twice in deionized water, immersed in 100% ethanol and air dried, and sections were encircled with a liquid-blocking pen. Sections were incubated with Protease III reagent for 30 min at 40°C, then washed twice with deionized water. Sections were incubated with commercially available probes against mouse Mmp3 , Col2a1 , and human MMP3 or a negative control probe for 2 hrs at 40°C. Colorimetric assays were performed per the manufacturer’s instructions. Analysis of scRNA-Seq data Fastq files from each experimental time point and mouse genotype were aligned to the reference genome (mm10) individually using CellRanger Software (10x Genomics). Individual datasets were aggregated using the CellRanger aggr command without subsampling normalization. The aggregated dataset was then analyzed using the R package Seurat 74 . The dataset was trimmed of cells expressing fewer than 750 genes, and genes expressed in fewer than 50 cells. The number of genes, number of unique molecular identifiers and percentage of mitochondrial genes were examined to identify outliers. As an unusually high number of genes can result from a ‘doublet’ event, in which two different cell types are captured together with the same barcoded bead, cells with > 6000 genes were discarded. Cells containing > 7.5% mitochondrial genes were presumed to be of poor quality and were also discarded. The gene expression values then underwent library-size normalization and normalized using established Single-Cell-Transform function in Seurat. Principal component analysis was used for dimensionality reduction, followed by clustering in principal component analysis space using a graph-based clustering approach via Louvain algorithm. UMAP was then used for two-dimensional visualization of the resulting clusters. Lineage inference was performed using Slingshot with available Slingshot software in R using converted Seurat object into singlecellexperiment objects. Analysis, visualization and quantification of gene expression and generation of gene module scores were performed using Seurat’s built-in function such as “FeaturePlot”, “VlnPlot”, “AddModuleScore”, and “FindMarker.” Lists of genes associated with each GO category were obtained from Geneontology.org. Panther / DAVID / GO / GREAT analysis was performed using web-based platform at Geneontology.org, Great.Stanford.Edu, and David.ncifcrf.gov. Top 1000 genes expressed in modulated SMC was defined by the highest expressing 1000 transcripts (based on average expression) from scRNA data in all de-differentiated lineage traced cells. Promoter/5’-Regulatory region of genes were extracted utilizing UCSC table browser based off 1kb upstream of TSS of transcripts. Motif analysis was performed using freely available HOMER software 75 with findMotifGenome function. The regulatory region of the top 1000 gene was used as the background as bases for motif enrichment. HCASMC culture/experiments Cells were cultured in smooth muscle growth medium (Lonza; catalog number: CC-3182) supplemented with human epidermal growth factor, insulin, human basic fibroblast growth factor and 5% FBS, according to the manufacturer’s instructions. All HCASMC lines were used at passages 4–8. siRNA knockdowns were performed using Lipofectamine RNAiMax (Life Technologies) using manufacturer’s recommended protocol at 50pg siRNA / 100,000 cells. Cells were allowed to recover in SMC growth medium (with or without additional growth factor) for 36 hours prior to RNA harvest. Recombinant TGFB (PeproTech 100 − 21) concentration used in stimulation was 10ng/ml. Proximity Ligation Assay Human coronary artery smooth muscle cells were cultured on tissue-culture slides in serum containing media for 24 hours. The cells were then fixed with 4% PFA for 30 minutes at room temperature. Proximity ligation assays were performed on these slides using a Sigma DuoLINK kit (DUO92101) with rabbit anti-SMAD3 antibody (Cell Signaling 9523S (1:200)), mouse anti-HOXB2 monoclonal antibody (DSHB: PCRP-HOXB2-1C9 (1:50 (hybridoma supernatant)), or mouse anti-SOX9 monoclonal antibody (DSHB PCRP-SOX9-1A2 (1:50 hybridoma supernatant)). Co-IP Experiment Myc-Flag-tagged SOX9 (PS100016 Origene) and 6x-HIS-tagged-HOXB2 (Addgene 8522) were obtained from commercial vendors and cloned into pCMV6 vector and transfected into HEK cells. The cells were allowed to recover for 36 hours after media change and Nuclear-Complex Co-IP was performed using commercially available Nuclear-Complex Co-IP kit from ActiveMotif(54001) with manufacturer’s recommended protocol using mouse Anti-Flag (Sigma F3165) or mouse Anti-His (Abcam 18184) antibody for immunoprecipitation, followed by blotting using Rabbit anti-Smad3 antibody (Cell Signaling 9523S), followed by Anti-Rabbit HRP (Cell Signaling 7074S) and detected via Luminata Forte Western HRP substrate (Millipore). Luciferase experiments For Luciferase experiments, an evolutionary conserved region of human MFAP5 regulatory region (chr12:8815212–8815569) was cloned from human genomic DNA and placed into pLuc-MCS vector, whereas inert/scramble similar length spacer was cloned into baseline pLuc-MCS as control. pCMV6-empty, and cloned pCMV6-Flag-SOX9 or pCMV6-his-HOXB2 were transfected into cells via lipofectamine 2000 along with respective luciferase and Renilla vector. Media was changed after 6 h, and dual-luciferase activity (Promega) was recorded after 24 h using a SpectraMax L luminometer (Molecular Devices). Relative luciferase activity (firefly/ Renilla luciferase ratio) is expressed as the fold change over control conditions. Mmp3 Activity Assay Mmp3 activity was measured via Abcam MMP-3 Activity Assay Kit (ab118972) following their tissue-based activity measurement protocol. 0.5 cm of dissected thoracic aorta from identical locations of control and Smad3 ΔSMC mice were placed in the tissue homogenizer for 10 seconds on ice in chilled assay buffer. After centrifugation, the supernatants were then directedly assayed. Mmp3 activity was measured at 10 minutes and 30 minutes after initiation of the reaction, using a SpectraMax luminometer at Ex/Em = 325/393 nm, with exposure of 600ms. Two biological replicates with 3 separate segments of thoracic aorta were used for the assay. Transwell Assay HCASMCs were grown in 24 well plates at low density at 10,000cells/well. SMAD3 knockdown was performed 24 hours prior to initiation of the migration experiment. HCASMCs were washed with PBS and cultured in serum free HCASMC media. 8um cell culture inserts (Corning 353097) were placed into the wells. THP-1 cells (ATCC) were grown in standard culture conditions. then spun down, and resuspended in serum-free HCASMC media, and placed in the top chamber for 3 hours at 37C. After 3 hours, THP1 cells in the bottom chamber in suspension were quantified. TUNEL Assay TUNEL assay was performed on cryopreserved aortic root sections using commercially available chromogenic TUNEL assay kits. Quantification was performed on 40X magnification at one random point on each cusp and TUNEL + nuclei was counted manually in a blinded manner. Statistical methods Differentially expressed genes in the scRNA-Seq data were identified using a Wilcoxon rank-sum test. Distribution of cells within defined-populations was tested via X-square test. Significance determination of histological measurement, luciferase studies, qPCR results, and composite gene-score were done via two-tailed T-test. Multiple comparisons were corrected via Bonferroni correction when necessary. Declarations Acknowledgements Special thanks to the Yana Ryan, Krista Hennig, Peter Mcguire and Hassan Chaib at the Stanford Genomic Sequencing and Service Center (GSSC) for performing 10x capture, library construction, and sequencing. We also thank the Stanford shared FACS facility for required FACS analysis and experiments. Also, thanks to the Matzuk lab for providing us with conditional Smad3 knockout mice. Illustrations were made with BioRender software. David Dichek, Univ. Washington, is acknowledged for advice regarding data interpretation. Funding This work was supported by National Institutes of Health grants F32HL143847 (PC), K08HL153798 (PC), K08HL152308 (RW), K08HL133375 (JBK), F32HL154681 (AP), R01AR066629 (MF), R01HL109512 (TQ), R01HL134817 (TQ), R33HL120757 (TQ), R01HL139478 (TQ), R01HL156846 (TQ), R01HL151535 (TQ), R01HL145708 (TQ), as well as a Human Cell Atlas grant from the Chan Zuckerberg Foundation. This work was also supported by American Heart Association grant 20CDA35310303 (PC) and 18CDA34110206 (RW). Competing interests The authors have no competing interests to declare. Author Contributions PC/TQ: Designing research studies, conducting experiments, acquiring data, analyzing data, providing reagents, and writing the manuscript. RW/JK/TN/RK: conduction experiments and acquiring data. QZ/AP/DS/DI/MF: analyzing data and other critical scientific input. Acknowledgements Special thanks to the Yana Ryan, Krista Hennig, Peter Mcguire and Hassan Chaib at the Stanford Genomic Sequencing and Service Center (GSSC) for performing 10x capture, library construction, and sequencing. We also thank the Stanford shared FACS facility for required FACS analysis and experiments. Also, thanks to the Matzuk lab for providing us with conditional Smad3 knockout mice. Illustrations were made with BioRender software. David Dichek, Univ. Washington, is acknowledged for advice regarding data interpretation. Funding This work was supported by National Institutes of Health grants F32HL143847 (PC), K08HL153798 (PC), K08HL152308 (RW), K08HL133375 (JBK), F32HL154681 (AP), R01AR066629 (MF), R01HL109512 (TQ), R01HL134817 (TQ), R33HL120757 (TQ), R01HL139478 (TQ), R01HL156846 (TQ), R01HL151535 (TQ), R01HL145708 (TQ), as well as a Human Cell Atlas grant from the Chan Zuckerberg Foundation. 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KLF4 (Kruppel-Like Factor 4)-Dependent Perivascular Plasticity Contributes to Adipose Tissue inflammation. Arterioscler Thromb Vasc Biol , ATVBAHA120314703, doi: 10.1161/ATVBAHA.120.314703 (2020). 68 Zhang, Y., Feng, X., We, R. & Derynck, R. Receptor-associated Mad homologues synergize as effectors of the TGF-beta response. Nature 383 , 168–172, doi: 10.1038/383168a0 (1996). 69 Finnson, K. W., Almadani, Y. & Philip, A. Non-canonical (non-SMAD2/3) TGF-beta signaling in fibrosis: Mechanisms and targets. Semin Cell Dev Biol 101 , 115–122, doi: 10.1016/j.semcdb.2019.11.013 (2020). 70 Pedroza, A. J. et al. Single-Cell Transcriptomic Profiling of Vascular Smooth Muscle Cell Phenotype Modulation in Marfan Syndrome Aortic Aneurysm. Arterioscler Thromb Vasc Biol 40 , 2195–2211, doi: 10.1161/ATVBAHA.120.314670 (2020). 71 Christersdottir, T. et al. Prevention of radiotherapy-induced arterial inflammation by interleukin-1 blockade. Eur Heart J 40 , 2495–2503, doi: 10.1093/eurheartj/ehz206 (2019). 72 Gomez, D. et al. Interleukin-1beta has atheroprotective effects in advanced atherosclerotic lesions of mice. Nat Med 24 , 1418–1429, doi: 10.1038/s41591-018-0124-5 (2018). 73 Ambrose, J. A. et al. Angiographic progression of coronary artery disease and the development of myocardial infarction. J Am Coll Cardiol 12 , 56–62, doi: 10.1016/0735-1097(88)90356-7 (1988). 74 Stuart, T. et al. Comprehensive Integration of Single-Cell Data. Cell 177 , 1888–1902 e1821, doi: 10.1016/j.cell.2019.05.031 (2019). 75 Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell 38 , 576–589, doi: 10.1016/j.molcel.2010.05.004 (2010) Additional Declarations There is NO Competing Interest. Supplementary Files SupMaterialNCR.pdf Supplemental Figure Legends Sup Fig 1: (A) Measured weight of experimental mice used in sections at time of sacrifice. (B) Percent of total experimental mice cohort that survived to final time point at 24 weeks. (C) Aligned captured mRNA sequence of tdTomato positive cells in control (top) and Smad3SMC tdTomato cells, showing absence of reads mapping to exon 2-3 which are flanked by LoxP sites. Sup Fig 2: Mesenchymal proliferation score of de-differentiated SMC in control and SMC specific Smad3SMC mice. Sup Fig 3: Number of TUNEL labeled apoptotic cells per high power field (HPF) in control and Smad3SMC mice. Sup Fig 4: (A) Expression of Mmp3 in lineage labeled (Cre positive) and non-lineage labelled (Cre negative) cells based on scRNAseq data. (B) Measured Mmp3 activity detected in isolated aortic tissue from wild type (brown) vs Smad3SMC (orange). (C) Featureplot of lineage-traced cells expressing Mmp3 in control and Smad3SMC aortic root. (D) High power view of section of atherosclerotic plaque with region of broken elastic lamina stained for Mmp3 expression (orange-brown color). (E) Fraction of transition SMC in control and Smad3SMC lesions with R-SMC fate as defined by unbiased clustering (left) and by concurrent high Mmp3/CxCl12 expression (right). (F) Number of migrated THP-1 cells after 3 hours of transwell-incubation with no cells, control HCASMCs, and SMAD3-deficient HCASMCs. Sup Fig 5: (A) Macrophage from control and Smad3SMC aortic root. B) Unbiased clustering of macrophages from control and knockout mice. C) Proportion of different macrophage subsets in control and Smad3SMC aortic root. D-F) Featureplots demonstrating macrophage cluster-specific markers. Sup Fig 6: A) Individual cell expression of HoxB2, HoxB3, HoxB4, and Sox9 in lineage labeled cells in control and Smad3SMC aortic root. B) FeaturePlot of HoxB2 (left) and Sox9 (right) demonstrate expression predominantly in SMC and fibroblasts. C) Control reporter luciferase activity in response to SOX9 and HOXB2 overexpression. Sup Fig 7: Additional replicates of Flag-SOX9 with endogenous SMAD3 IP with additional controls for anti-flag antibody. Supplemental Files Supplementary Table 1: Differentially expressed genes in R-SMC vs other transition SMC. Supplementary Table 2: Differentially expressed genes in transition SMC in Smad3SMC vs control. Cite Share Download PDF Status: Published Journal Publication published 13 Apr, 2022 Read the published version in Nature Cardiovascular Research → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-708882\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":39774717,\"identity\":\"41b516b4-56e3-41a4-b838-7a88f6837d6c\",\"order_by\":0,\"name\":\"Paul Cheng\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0003-3429-2702\",\"institution\":\"Stanford 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University\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Michael\",\"middleName\":\"\",\"lastName\":\"Fischbein\",\"suffix\":\"\"},{\"id\":39774728,\"identity\":\"02cdb763-4b07-482f-a8db-d82620ee625a\",\"order_by\":11,\"name\":\"Thomas Quertermous\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYLCChAoQyfiAgYENSB8goJoHRDw4wyDBwMBsQLwWxodtpGixZ+9OvJE4r66Of3Yz4+OKMgY5vhsJBGzhObvZInHbYQmJO4eZDc+cYzCWJKhFInebROK2AxIMN/KPSTa2MSRuIE7LnDoJ+RvJbCAt9URqaWCWMIBqSTAgqOUM0C8Jxw5LbryRzGzYcE7CcOaZB/i1sLf3brz5o6aOX+5GMuPDhjIbeb7jBGwBAQkcbCK1jIJRMApGwSjABADadUSP3XlG7wAAAABJRU5ErkJggg==\",\"orcid\":\"https://orcid.org/0000-0002-7645-9067\",\"institution\":\"Stanford University\",\"correspondingAuthor\":true,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Thomas\",\"middleName\":\"\",\"lastName\":\"Quertermous\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2021-07-12 03:05:40\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-708882/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-708882/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1038/s44161-022-00042-8\",\"type\":\"published\",\"date\":\"2022-04-13T04:00:00+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":11600399,\"identity\":\"2c8e14c5-f7ce-4526-9324-83fa4cc204ef\",\"added_by\":\"auto\",\"created_at\":\"2021-07-19 16:19:43\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":124017,\"visible\":true,\"origin\":\"\",\"legend\":\"Smad3DSMC mice have increased lesion burden in the ApoE null model. \\n\\n(A) Representative figure of mouse protocol showing SMC-specific lineage tracing and Smad3 conditional knockout (Smad3DSMC). (B) Smad3DSMC SMC transition cells can contribute to all regions of the lesion, including the lesion cap (arrow), modulated SMC (*), and tunica media (m). (C) Representative sections from control and Smad3DSMC mice stained for Tagln to highlight cap and tunica media for lesion quantification. (D) Lesion area between the lumen and internal elastic lamina, (E) lumen area, (F) vessel area quantified as area between lumen and external elastic lamina, across experimental animals. (G) Representative sections from control and Smad3DSMC mice revealing tdT fluorescence to show lineage traced cells in the media and plaque. (H) Quantification of tdT positive area in control and Smad3DSMC mice. (I) Representative sections from control and Smad3DSMC mice stained with CD68 antibody. (J) Quantification of CD68 positive area,* p\\u003c0.05. Each dot from each bar graph represents data measured from a single matched section from each individual animal. Error bars represent 95% CI of mean. \",\"description\":\"\",\"filename\":\"1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-708882/v1/28daa3b779844d8570422406.jpg\"},{\"id\":11600059,\"identity\":\"3f1b01f8-75c0-430a-9e7e-82597cef97ed\",\"added_by\":\"auto\",\"created_at\":\"2021-07-19 16:16:43\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":113410,\"visible\":true,\"origin\":\"\",\"legend\":\"Loss of Smad3 alters cell fate decisions of transition SMC in atherosclerotic lesions. \\n\\n(A) Expression of lineage tracing marker tdTomato, mature SMC markers Myh11, Cnn1, and unbiased clustering identified transition SMC in UMAP depiction of scRNAseq data. (B) Fraction of lineage traced cells that were mature SMC as defined by Myh11, Cnn1 expression or unbiased clustering. (C) Expression of key lineage markers of other disease relevant cells captured by our scRNA sequencing, including fibromyocytes (Tnfrsf11b), endothelial cells (Cdh5), macrophages (CD68), and lymphatic endothelial cells (Prox1). (D) Unbiased clustering of all captured cells and respective clustering upon applying the Louvain algorithm, represented in UMAP space, with their respective biological identities as defined by marker genes. (E) Fraction of de-differentiated lineage-traced cells that contribute to fibromyocytes (FMC), chondromyocytes (CMC), and the newly identified cell cluster in control and Smad3DSMC mice. (F) Fraction of lineage traced cells that contribute to CMC in control and Smad3DSMC mice. (G) CMC marker Col2a1 expression by RNAscope, positive area per section in control and Smad3DSMC mice. (H) Representative images of colorimetric Col2a1 RNAscope quantification with control and Smad3DSMC mice. (I) Von Kossa staining of sections from control and Smad3DSMC mice, with (J) quantification as area of staining. (K) Dot plot representation of expression of cell-cycle markers in transitional SMC as represented by relative expression (color) and fraction of total positive cells (size). (L) Chondrocyte proliferation score of control and Smad3DSMC transitional SMC. * p\\u003c0.05, ** p\\u003c0.01. \",\"description\":\"\",\"filename\":\"2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-708882/v1/5ea15092f4571008893a9dac.jpg\"},{\"id\":11600400,\"identity\":\"39f506f7-ae77-488e-8891-ec6036b15779\",\"added_by\":\"auto\",\"created_at\":\"2021-07-19 16:19:43\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":145054,\"visible\":true,\"origin\":\"\",\"legend\":\"The novel cluster of Mmp3-expressing R-SMC transition cells promote remodeling and inflammation. A) UMAP representation and location of the novel cluster of cells with high Mmp3 expression. (B) Percent of tdTomato positive cells representing different cellular clusters. (C) Pseudotemporal alignment of cells with Slingshot lineage inference (solid black line) identified a likely relationship for SMC, FMC, CMC, and R-SMC. (D) Heatmap of differentially regulated genes among distinct lineage-traced SMC in the lesion. Differentially expressed genes between CMC and R-SMC are indicated by yellow boxes. (E) Normalized Mmp3 expression of all the cells in the lesion. (F) Immunohistochemistry of Mmp3 in control and Smad3DSMC mice. (G) RNAscope of Mmp3 (red) and Col2a1 (blue) expressing cells in atherosclerotic lesion showing distinct localization and clustering. (H) RNAscope for MMP3 expression in human coronary artery specimens. (I) Relative MMP3 expression in HCASMC in the presence and absence of TGFb and SMAD3. (J) Biological processes enriched in genes that are preferentially expressed by R-SMC compared to other transitional SMC. (K) Expression of chemoattractants (Cxcl12, Saa3, Ccl2) that are specific to R-SMC with their respective receptors (Cxcr4, Tlr1, Ccr2) showing expression restricted to the monocyte cluster, * p\\u003c0.05. Each dot from each bar graph represents a single qPCR measurement from each biological replicate. Error bars represent SEM. \",\"description\":\"\",\"filename\":\"3.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-708882/v1/f3c79e847fed07931cd414f6.jpg\"},{\"id\":11600058,\"identity\":\"407045f7-ef11-4a2e-ae4f-a6cca4baf359\",\"added_by\":\"auto\",\"created_at\":\"2021-07-19 16:16:43\",\"extension\":\"jpg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":150967,\"visible\":true,\"origin\":\"\",\"legend\":\"Smad3 regulates a transcriptional program associated with vascular outward expansion in conjunction with Sox9 and HoxB2. (A) Significantly enriched GO biological processes represented by Smad3 differentially regulated genes. (B) Enrichment of human disease terms associated with differentially regulated genes. (C) Relative expression of LOX and MFAP5 in HCASMC in the presence and absence of TGFb, in cells transfected with siRNA against SMAD3 (SMAD3KD) or non-targeting control. (D) HOMER analysis results for motifs enriched in promoter regions of genes differentially regulated in Smad3DSMC mice. (E) Expression of MMP3 and MFAP5 in control and SOX9 or HOXB2 knockdown HCASMC. (F) Co-immunoprecipitation of His-tagged HOXB2 (lane 4/5) or Flag-tagged SOX9 (lane 6) with endogenous SMAD3 compared to IgG or control vector (lanes 2/3). (G) Proximity ligation assay demonstrates close proximity of HOXB2-SMAD3 and SOX9-SMAD3 in the nucleus of HCASCMC. (H) Relative luciferase activity of MFAP5-Luc in HEK-293 cells transfected with HOXB2 and SOX9 expression vectors in the absence (left) or presence (right) of SMAD3 siRNA (SMAD3KD) or control siRNA (control), *p\\u003c0.05. Each dot from each bar graph represents a single normalized luciferase measurement from each biological replicate. Error bars represent SEM. \",\"description\":\"\",\"filename\":\"4.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-708882/v1/1b778d9021ecce9b2185290b.jpg\"},{\"id\":11600056,\"identity\":\"5602d676-ea66-42be-93ae-45130f403884\",\"added_by\":\"auto\",\"created_at\":\"2021-07-19 16:16:43\",\"extension\":\"jpg\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":49031,\"visible\":true,\"origin\":\"\",\"legend\":\"Smad3 regulates smooth muscle cell fate and governs adverse remodeling and calcification of atherosclerotic plaque.\",\"description\":\"\",\"filename\":\"5.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-708882/v1/f5ad2460abe7dfd6e328b8b6.jpg\"},{\"id\":20334480,\"identity\":\"19fc4369-7c4d-4467-aafe-88c9e661f786\",\"added_by\":\"auto\",\"created_at\":\"2022-04-14 09:50:51\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":832172,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-708882/v1/d3e67d14-3878-45ad-b20d-ba86a8e89cf2.pdf\"},{\"id\":11600061,\"identity\":\"d197be74-c9d1-46ba-8867-1c5211e4f5c2\",\"added_by\":\"auto\",\"created_at\":\"2021-07-19 16:16:44\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":6346958,\"visible\":true,\"origin\":\"\",\"legend\":\"Supplemental Figure Legends\\nSup Fig 1: (A) Measured weight of experimental mice used in sections at time of sacrifice. (B) Percent of total experimental mice cohort that survived to final time point at 24 weeks. (C) Aligned captured mRNA sequence of tdTomato positive cells in control (top) and Smad3SMC tdTomato cells, showing absence of reads mapping to exon 2-3 which are flanked by LoxP sites. \\n\\nSup Fig 2: Mesenchymal proliferation score of de-differentiated SMC in control and SMC specific Smad3SMC mice. \\n\\nSup Fig 3: Number of TUNEL labeled apoptotic cells per high power field (HPF) in control and Smad3SMC mice. \\n\\nSup Fig 4: (A) Expression of Mmp3 in lineage labeled (Cre positive) and non-lineage labelled (Cre negative) cells based on scRNAseq data. (B) Measured Mmp3 activity detected in isolated aortic tissue from wild type (brown) vs Smad3SMC (orange). (C) Featureplot of lineage-traced cells expressing Mmp3 in control and Smad3SMC aortic root. (D) High power view of section of atherosclerotic plaque with region of broken elastic lamina stained for Mmp3 expression (orange-brown color). (E) Fraction of transition SMC in control and Smad3SMC lesions with R-SMC fate as defined by unbiased clustering (left) and by concurrent high Mmp3/CxCl12 expression (right). (F) Number of migrated THP-1 cells after 3 hours of transwell-incubation with no cells, control HCASMCs, and SMAD3-deficient HCASMCs. \\n\\nSup Fig 5: (A) Macrophage from control and Smad3SMC aortic root. B) Unbiased clustering of macrophages from control and knockout mice. C) Proportion of different macrophage subsets in control and Smad3SMC aortic root. D-F) Featureplots demonstrating macrophage cluster-specific markers.\\n\\nSup Fig 6: A) Individual cell expression of HoxB2, HoxB3, HoxB4, and Sox9 in lineage labeled cells in control and Smad3SMC aortic root. B) FeaturePlot of HoxB2 (left) and Sox9 (right) demonstrate expression predominantly in SMC and fibroblasts. C) Control reporter luciferase activity in response to SOX9 and HOXB2 overexpression.\\n\\nSup Fig 7: Additional replicates of Flag-SOX9 with endogenous SMAD3 IP with additional controls for anti-flag antibody. \\n\\nSupplemental Files\\nSupplementary Table 1: Differentially expressed genes in R-SMC vs other transition SMC.\\nSupplementary Table 2: Differentially expressed genes in transition SMC in Smad3SMC vs control.\\n\",\"description\":\"\",\"filename\":\"SupMaterialNCR.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-708882/v1/804929a1296ef8ec73917d91.pdf\"}],\"financialInterests\":\"There is \\u003cb\\u003eNO\\u003c/b\\u003e Competing Interest.\",\"formattedTitle\":\"\\u003cp\\u003e\\u003cem\\u003eSmad3\\u003c/em\\u003e Regulates Smooth Muscle Cell Fate and Governs Adverse Remodeling and Calcification of Atherosclerotic Plaque\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eDecades of research and drug development have led to therapies and interventions that have significantly diminished morbidity and mortality from cardiovascular disease \\u003csup\\u003e1,2\\u003c/sup\\u003e. However, coronary artery disease (CAD) remains a leading cause of death in this country and worldwide with a sharp decline in the rate of improvement in mortality observed over the past decade \\u003csup\\u003e3\\u003c/sup\\u003e. Recent clinical studies targeting well-characterized risk factors such as lipids \\u003csup\\u003e4,5\\u003c/sup\\u003e and novel targets related to inflammation \\u003csup\\u003e6,7\\u003c/sup\\u003e have had modest results \\u003csup\\u003e8\\u003c/sup\\u003e suggesting a continued need to identify new disease modifiers. Over the past decade, genome wide association studies (GWAS) have identified over 160 loci that contribute to CAD risk \\u003csup\\u003e9,10\\u003c/sup\\u003e. Causal variation identified in these loci point primarily to genes and pathways predicted to function in the blood vessel wall to regulate disease risk \\u003csup\\u003e11\\u0026ndash;13\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eSpecific features of atherosclerotic plaque have been increasingly recognized to offer significant prognostic value. For instance, it has been noted that cellular composition, such as SMC contribution to the fibrous cap, influences risk of plaque rupture \\u003csup\\u003e14\\u0026ndash;16\\u003c/sup\\u003e. With advances in diagnostic imaging, novel features such as positive remodeling and microcalcification have been found to be highly predictive for myocardial infarctions \\u003csup\\u003e17,18\\u003c/sup\\u003e. Although counter-intuitive, studies using both intravascular ultrasound (IVUS), and longitudinal studies using CT coronary angiography \\u003csup\\u003e17,18\\u003c/sup\\u003e, have demonstrated that sites of outward remodeling are much more likely to be the culprit site for plaque rupture and myocardial infarction compared to sites with more luminal narrowing. However, little is known regarding the cellular and molecular mechanisms by which these high-risk features are controlled.\\u003c/p\\u003e \\u003cp\\u003eA number of post-genomic GWAS follow-up studies in this and other laboratories investigating the genetic disease-related mechanisms of CAD have focused on SMC, and the relationship of SMC cell state changes to disease risk \\u003csup\\u003e19\\u0026ndash;23\\u003c/sup\\u003e. These studies have indicated that a significant portion of the CAD attributable risk is determined by this cell type \\u003csup\\u003e13\\u003c/sup\\u003e. Consistent with this notion, recent lineage tracing studies have demonstrated that the majority of cells inside atherosclerotic plaque, including those expressing some inflammatory markers, are oligo-clonal de-differentiated smooth muscle derivatives \\u003csup\\u003e24\\u0026ndash;27\\u003c/sup\\u003e. Genes that alter SMC behavior are known to influence the composition of atherosclerotic plaque \\u003csup\\u003e19,22,23,26,28\\u003c/sup\\u003e. CAD-associated genes \\u003cem\\u003eTCF21\\u003c/em\\u003e and \\u003cem\\u003eAHR\\u003c/em\\u003e have been linked to these processes, and various genomic data suggests that additional CAD genes might also regulate SMC phenotype \\u003csup\\u003e21,28\\u0026minus;32\\u003c/sup\\u003e. While these SMC progeny have been previously lumped together as phenotypically modulated SMC, advances in single cell RNA profiling has demonstrated the presence of subsets of these cells with distinct transcriptomes and cell fates. For example, medial SMC have been shown to give rise to fibroblast-like cells termed fibromyocytes \\u003csup\\u003e23\\u003c/sup\\u003e, as well as cells similar to endochondral bone forming cells, chondromyocytes \\u003csup\\u003e19,30\\u003c/sup\\u003e, among other bioinformatically defined populations \\u003csup\\u003e19,33\\u003c/sup\\u003e. However, the functional significance and relative location of these transcriptionally distinct populations remain to be elucidated.\\u003c/p\\u003e \\u003cp\\u003eThe TGFβ signaling pathway is central to smooth muscle biology during development and disease, and an important modifier of atherosclerosis \\u003csup\\u003e34,35\\u003c/sup\\u003e. Canonical TGFβ signaling is thought to be mediated through Smad family proteins, particularly nuclear signaling factors Smad2 and Smad3. While themselves poor binders to DNA \\u003csup\\u003e36\\u003c/sup\\u003e, through their interaction with other transcription factors, the Smad factors are central to key transcriptional programs that regulate cell fate in development and disease \\u003csup\\u003e37,38\\u003c/sup\\u003e. Multiple GWAS have identified rs17293632 \\u003csup\\u003e9\\u003c/sup\\u003e, a single nucleotide variant that lies within a functional smooth muscle enhancer that regulates SMAD3 expression \\u003csup\\u003e20\\u003c/sup\\u003e, as an important modifier of risk for myocardial infarction. However, how smooth muscle \\u003cem\\u003eSMAD3\\u003c/em\\u003e expression influences risk of myocardial infarction is unclear. \\u003cem\\u003eIn vitro\\u003c/em\\u003e, SMAD3 appears to modify smooth muscle cell differentiation and proliferation through its interaction with other transcription factors critical to SMC biology and risk of CAD \\u003csup\\u003e29\\u003c/sup\\u003e. However, the exact effects of \\u003cem\\u003eSmad3\\u003c/em\\u003e expression level on SMC plaque biology remain unknown.\\u003c/p\\u003e \\u003cp\\u003eHere, we demonstrate that SMC-specific deletion of \\u003cem\\u003eSmad3\\u003c/em\\u003e influences the fate of de-differentiated SMC in atherosclerotic plaques \\u003cem\\u003ein vivo\\u003c/em\\u003e, promoting both a new pro-remodeling SMC transition phenotype that expresses remodeling genes such as \\u003cem\\u003eMmp3\\u003c/em\\u003e and inflammatory chemokines such as \\u003cem\\u003eCxcl12, as well as\\u003c/em\\u003e an expansion of the SMC-derived chondromyocyte (CMC) population. These cellular changes are associated with increased positive remodeling and plaque calcification that appear to be directed by Smad3 in conjunction with transcriptional effects of Hox and Sox factors.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cspan class=\\\"ItalicUnderline\\\" name=\\\"Emphasis\\\" type=\\\"ItalicUnderline\\\"\\u003eSMC-specific deletion of Smad3 is associated with increased lesion burden, outward remodeling, and increased numbers of both SMC progeny and monocyte/macrophage lineage cells\\u003c/span\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo understand how smooth muscle expression of \\u003cem\\u003eSmad3\\u003c/em\\u003e influences atherosclerotic lesions \\u003cem\\u003ein vivo\\u003c/em\\u003e, we generated a murine model of atherosclerosis with established smooth-muscle specific Cre (\\u003cem\\u003eMyh11-Cre\\u003c/em\\u003e) crossed with a conditional knockout allele of \\u003cem\\u003eSmad3\\u003c/em\\u003e \\u003csup\\u003e39\\u0026ndash;41\\u003c/sup\\u003e with concurrent lineage tracing provided by conditional tandem dimer tomato (\\u003cem\\u003eROSA\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003eTdt\\u003c/em\\u003e\\u003c/sup\\u003e) expression on the \\u003cem\\u003eApoE\\u003c/em\\u003e null background (\\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e) (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA). To limit confounding created by the critical role of Smad3 during development, the \\u003cem\\u003eSmad3\\u003c/em\\u003e gene was deleted via a tamoxifen inducible Cre only after mice had reached maturity (8-weeks-old), immediately prior to initiation of a Western high-fat diet (HFD, Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA). The \\u003cem\\u003eSmad3\\u003c/em\\u003e conditional knockout mice grew to maturity with no significant change in weight or mortality compared to control (Suppl. Figure\\u0026nbsp;1A, B), with highly efficient Cre-mediated deletion of the floxed DNA binding domain of \\u003cem\\u003eSmad3\\u003c/em\\u003e (Suppl Fig.\\u0026nbsp;1C).\\u003c/p\\u003e\\n\\u003cp\\u003eExamination of atherosclerotic lesions in the aortic root after 16 weeks of HFD demonstrated that SMC from \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e mice were able to migrate into the lesion, expand, and contribute to the formation of atherosclerotic plaque and the fibrous cap (Figs.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB, \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eC). Quantification of atherosclerotic lesions revealed a significant increase in plaque volume in \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e compared to control animals (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eD). To further characterize the anatomy of diseased vessels, specifically regarding outward remodeling vs luminal narrowing, we evaluated the area encapsulated by the diseased vessel as well as lumen area. The lumen area in these sections showed no significant change (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eE), but the area circumscribed by the external elastic lamina was significantly increased (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eF). These findings are consistent with expansion of atherosclerotic plaque volume in conjunction with outward \\u0026ldquo;positive\\u0026rdquo; remodeling. To determine the cellular anatomy associated with the increased plaque size, we quantified the area occupied by fluorescent tdTomato SMC lineage traced cells (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eG, \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eH) as well as CD68 stained monocytes and macrophages in the lesions (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eI, \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eJ). This analysis revealed a statistically significant increase in area for both SMC-derived cells as well as cells of the monocyte-macrophage lineage. Given that the lineage tracing \\u003cem\\u003eMyh11-Cre\\u003c/em\\u003e transgene is active only in cells emanating from mature SMC, these findings suggest that the increased lesion growth has both a cell autonomous as well as a non-autonomous cellular component, with the latter reflecting an SMC mediated effect on monocyte-macrophage lesion recruitment.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cspan class=\\\"ItalicUnderline\\\" name=\\\"Emphasis\\\" type=\\\"ItalicUnderline\\\"\\u003eSingle cell transcriptomic profiling reveals Smad3 deletion alters SMC fate to promote a pro-remodeling and chondromyocyte phenotype\\u003c/span\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eGiven the critical role that \\u003cem\\u003eSmad3\\u003c/em\\u003e plays in cell fate decisions during development, we hypothesized that alteration in disease-associated SMC phenotype transitions might account for the observed cellular lesion characteristics as well as recruitment of CD68\\u0026thinsp;+\\u0026thinsp;cells and positive remodeling. Thus, to better understand how loss of \\u003cem\\u003eSmad3\\u003c/em\\u003e expression produced phenotypic changes in lesion SMC derived cells, and their interactions with other lesion cell types, we performed single cell RNA expression profiling (scRNAseq) of atherosclerotic lesions from \\u003cem\\u003eSmad3\\u003c/em\\u003e knockout (\\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e) and control animals. The atherosclerotic tissue was harvested, processed for single cell encapsulation, RNA capture, reverse transcription and amplification with the 10X Genomics Chromium V3 platform, and cDNA libraries were sequenced as previously described \\u003csup\\u003e23,28\\u003c/sup\\u003e. Subsequently, the single cell expression data were visualized utilizing Uniform Manifold Approximation and Projection (UMAP) to create a 2-D projection representing the organization of individual cells to each other in clusters, and of the relationship of the organized clusters to each other. After filtering and normalization, a total of 26,219 cells from control and 35,518 cells from \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e mice were included in the analysis obtained from 3 and 4 independent captures of pooled tissue from 2 mice in each individual capture.\\u003c/p\\u003e\\n\\u003cp\\u003eWe first investigated clustering of the scRNAseq data using standard settings for the principal component analysis and number of clusters in UMAP space. Feature plots were employed to visualize the SMC lineage traced cells (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA). SMC traced cells were identified by expression of the \\u003cem\\u003etdTomato\\u003c/em\\u003e gene, and the component of this cluster representing mature medial SMC was identified by expression of SMC lineage markers \\u003cem\\u003eMyh11\\u003c/em\\u003e and \\u003cem\\u003eCnn1\\u003c/em\\u003e. As we have shown previously, a significant portion of the SMC-lineage traced cells during disease did not express these mature SMC markers and thus represented SMC that had undergone phenotypic transition \\u003csup\\u003e22,23,28\\u003c/sup\\u003e. We were thus able to determine whether there were alterations in the number of mature differentiated versus transition SMC in \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e mice. We investigated whether there was an alteration in the number of differentiated SMC among the lineage traced (tdTomato+) cells, defining \\u0026ldquo;differentiated\\u0026rdquo; cells using classical \\u003cem\\u003eMyh11\\u003c/em\\u003e or \\u003cem\\u003eCnn1\\u003c/em\\u003e expression, along with unbiased clustering (SMC vs transition SMC) (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA, \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB). Cells were clustered based on the scRNAseq data using the Lovain algorithm to a resolution where the SMC derived cells are split into two distinct clusters, as we and other have characterized \\u003csup\\u003e23\\u003c/sup\\u003e. Regardless of the definition of \\u0026ldquo;mature SMC\\u0026rdquo;, there was a significant decrease in the proportion of mature SMC in the \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e compared to control mice (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA, \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB).\\u003c/p\\u003e\\n\\u003cp\\u003eTo discern the optimum biologically relevant resolution of clustering and avoid over-fitting with the Lovain algorithm, the clustering resolution was empirically determined as the minimal settings that allowed for separation of known biologically distinct populations of endothelial cells (EC) (Figs.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eC, \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eD), i.e., blood vessel EC (VE-cadherin expressing, Prox1 negative) vs lymphatic EC (Prox1 expressing) endothelial cells \\u003csup\\u003e42,43\\u003c/sup\\u003e. The identities of the cellular subpopulations that were created with this approach were then determined based on specifically expressed genes in each cellular cluster with the monocyte-macrophage lineage identified with CD68 expression, and the previously described SMC transition fibromyocytes by the marker \\u003cem\\u003eTnfrsf11b\\u003c/em\\u003e (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eC). The validity of this clustering approach was further confirmed via visualization of cell population canonical \\u0026ldquo;markers\\u0026rdquo; for all clusters in the UMAP space. Previous work in SMC lineage tracing in atherosclerosis has identified three distinct clusters of SMC derived cells, including mature SMC, fibromyocytes (FMC), and pro-calcific chondromyocytes (CMC) \\u003csup\\u003e19,28,33,44\\u003c/sup\\u003e. Similar subsets of lineage traced SMC were identified in these data (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eD), and in addition two distinct new clusters of Myh11-Cre lineage labeled populations were also identified. One cluster was composed of pericytes, and an additional small population of novel disease-associated SMC-derived cells clustered by itself as a unique transcriptomic phenotype, that we named \\u0026ldquo;remodeling-SMC\\u0026rdquo; (R-SMC), to be studied in more detail below.\\u003c/p\\u003e\\n\\u003cp\\u003eTo determine if loss of \\u003cem\\u003eSmad3\\u003c/em\\u003e expression alters the cell fate transitions of disease-associated SMC lineage cells, we ascertained the fraction of de-differentiated cells that contribute to clusters for each of the two known and the novel transition phenotypes. \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e transition SMC showed an increase in proportion of CMC along with the newly defined population at the expense of the more differentiated FMC (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eE, F). The increase in fraction of CMC among disease-associated \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e cells corresponded with a significant increase in lesion area expressing chondromyocyte marker \\u003cem\\u003eCol2a1\\u003c/em\\u003e (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eG, H). Functionally, this increase in \\u003cem\\u003eCol2a1\\u003c/em\\u003e expressing cells correlated with an increase in lesion calcified area as assayed by von Kossa staining of atherosclerotic lesions (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eI, J). These cells appeared to result from an increase in proliferation of de-differentiated SMC, as evidenced by a higher level and increased proportion of cells expressing proliferation markers such as \\u003cem\\u003eMki67, Ccnd1, Ccnb1\\u003c/em\\u003e, and \\u003cem\\u003eMyc\\u003c/em\\u003e, with limited alteration in cell-cycle inhibitor genes \\u003cem\\u003eCdkn2a/b\\u003c/em\\u003e (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eK). To further assess this possibility, we generated a \\u0026ldquo;chondrocyte proliferation score\\u0026rdquo; that represents a scaled-average expression of all genes associated with GO category \\u0026ldquo;promote chondrocyte proliferation\\u0026rdquo; with each individual cells\\u0026rsquo; transcriptome \\u003csup\\u003e45\\u003c/sup\\u003e. Consistent with higher expression of specific cell-cycle regulators, \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e transition SMC had a significantly higher chondrocyte proliferation score than control cells (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eL). Applying the analysis utilizing a \\u0026ldquo;mesenchymal proliferation score\\u0026rdquo; based on GO categories of \\u0026ldquo;promote mesenchymal proliferation\\u0026rdquo; produced equally significant results (Sup. Figure\\u0026nbsp;2), further suggesting that the increase in number of tdTomato positive cells in the lesions likely reflects proliferation of SMC derivative cells. The increase in cell number did not result from alterations in apoptosis, since there was no difference in TUNEL staining of control vs \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e sections (Sup. Fig, 3).\\u003c/p\\u003e\\n\\u003cdiv class=\\\"Section2\\\" id=\\\"Sec3\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eA novel pro-remodeling SMC phenotype is characterized by expression of Mmp3 and leukocyte recruiting factors\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eIn addition to increased CMC in the \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e animals, there was also an increase in the proportion of transition SMC that contributed to the newly identified cell cluster (Figs.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eD, \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA), that we refer to as remodeling-SMC (R-SMC) due to their high expression of genes involved in extracellular matrix remodeling. These cells were identified at the junction of CMC, fibroblast and FMC clusters. The vast majority of cells within this cluster were lineage traced at a proportion similar to that seen with CMC and FMC (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB) confirming they were SMC derived. In vascular lesions, they constituted 6% of SMC transition cells in \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e. While their gene expression pattern is most similar to that identified in CMC as shown by their juxtaposition in UMAP space and by hierarchical clustering, they were easily distinguished from CMC at the transcriptional level (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD). To investigate the ontogenic relationship of this group of cells to SMC, FMC, and CMC, we ported the scRNAseq data to Slingshot \\u003csup\\u003e46\\u003c/sup\\u003e, a lineage inference tool designed to map trajectories involving multiple branching lineages. Applying this algorithm suggested that SMC give rise to FMC which in turn serve as a source for both CMC and R-SMC (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC).\\u003c/p\\u003e\\n \\u003cp\\u003eInterestingly, the R-SMC cell population was marked by a particularly high expression of matrix metalloproteinase-3 (\\u003cem\\u003eMmp3\\u003c/em\\u003e) (Figs.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eE, \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eF), an enzyme required for outward remodeling of atherosclerotic vessels \\u003csup\\u003e47\\u003c/sup\\u003e. \\u003cem\\u003eMmp3\\u003c/em\\u003e was in fact among the most upregulated genes in this population compared to other SMC transition groups. Given our observation of increased outward remodeling identified in \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e mice, this finding was further investigated. Single-cell transcriptomic data suggested a higher number of \\u003cem\\u003eMmp3\\u003c/em\\u003e-expressing cells and an overall higher level of \\u003cem\\u003eMmp3\\u003c/em\\u003e expression in \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e than control cells (Sup. Figure\\u0026nbsp;4A, 4C). In addition to increased mRNA levels, an in vitro functional study utilizing a florescent based Mmp3-activity assay demonstrated that homogenized aortic tissue from \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e mice has more Mmp3 activity than control tissue (Sup Fig.\\u0026nbsp;4B).\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eIn situ\\u003c/em\\u003e hybridization with RNAscope showed \\u003cem\\u003eMmp3\\u003c/em\\u003e-expressing R-SMC to be a distinct population from CMC, which were marked by \\u003cem\\u003eCol2a1\\u003c/em\\u003e expression (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eG), indicating that these R-SMC transition cells are juxtaposed but not overlapping in the lesion plaque and further supporting their distinct phenotype. Consistent with the established role for MMP3 in positive remodeling, its expression was most prominent at the base of the atherosclerotic lesion in cells juxtaposed to the elastic lamina. Strikingly, these MMP3 cells were commonly associated with areas of disrupted elastic lamina, consistent with their possible invasion through this structure (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eF, \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eG, Sup Fig.\\u0026nbsp;4D). Importantly, the same rare population of MMP3 expressing cells were also found in human coronary arteries, where they were also associated with regions of disrupted elastic lamina (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eH). There were more cells expressing Mmp3 in \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e compared to controls as per the scRNAseq data and more prominent staining in \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e vs control (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eF, Sup Fig.\\u0026nbsp;4A). \\u003cem\\u003eMmp3\\u003c/em\\u003e expression was altered only in the SMC derived cells, and not significantly different in the non-SMC derived population, suggesting that altered Mmp3 levels by Smad3 happens in a cell-autonomous manner in SMC lineage transition cells (Sup Fig.\\u0026nbsp;4A). By performing TGF-\\u0026beta; stimulation of human coronary artery smooth muscle cells, we were able to show that MMP3 is suppressed by TGF-\\u0026beta; (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eI). SMAD3 knockdown did not significantly increase the expression of MMP3 at baseline, but did negate the TGF-\\u0026beta; dependent suppression of expression, suggesting SMAD3 is required for the TGF-\\u0026beta; dependent regulation of MMP3 in atherosclerotic lesions.\\u003c/p\\u003e\\n \\u003cp\\u003eTo better understand the vascular function of this cell population, we performed pathway analyses of all significantly differentially expressed genes in the R-SMC as compared to other de-differentiated SMC phenotypes (Sup Table\\u0026nbsp;1). PANTHER analysis revealed the top biological processes enriched in this list of genes are related to remodeling of ECM followed by regulation of chemotaxis and inflammation (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eJ). Analysis of genes contributing to the enrichment of these pathways reveal that this population of cells express higher levels of multiple chemoattractants whose receptors are primarily restricted to macrophages (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eK). For example, this population is the main SMC source of Cxcl12 in the lesion, with the Cxcr4 receptor for this ligand being expressed exclusively by lesion macrophages. In addition, other R-SMC differentially expressed cytokine genes, including \\u003cem\\u003eSaa3\\u003c/em\\u003e, and \\u003cem\\u003eCcl2\\u003c/em\\u003e, all have well-established roles in monocyte recruitment, with their respective receptor genes \\u003cem\\u003eTlr1\\u003c/em\\u003e and \\u003cem\\u003eCcr2\\u003c/em\\u003e expressed mostly by inflammatory cells in the lesion (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eK). There is high overlap between the \\u003cem\\u003eMmp3\\u003c/em\\u003e high cells and cytokine expressing cells. In fact, if a marker-based definition of a pro-remodeling and recruitment population were defined as cells with elevation of \\u003cem\\u003eMmp3\\u003c/em\\u003e and \\u003cem\\u003eCxcl12\\u003c/em\\u003e, one can re-capitulate the unbiased clustering results (Sup Fig.\\u0026nbsp;4E). Consistent with the hypothesis that SMAD3 regulates the SMC chemotaxis signal to inflammatory cells, \\u003cem\\u003ein vitro\\u003c/em\\u003e transwell migration studies demonstrated augmentation of HCASMC ability to recruit human monocytes (Sup Fig.\\u0026nbsp;4F). Taken together, these findings suggest that beyond its role in positive remodeling, this novel population of cells may orchestrate monocyte recruitment and underlie the increase in CD68 positive cells in lesions.\\u003c/p\\u003e\\n \\u003cp\\u003eGiven the increased monocyte-macrophage recruitment in \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e plaques, we investigated the cellular phenotypes and gene expression patterns in these cells. Three of these clusters were distinguished by expression of \\u003cem\\u003eCd168\\u003c/em\\u003e, \\u003cem\\u003eSpp1\\u003c/em\\u003e, and \\u003cem\\u003eCcr1\\u003c/em\\u003e, and a fourth cluster with a general macrophage lineage pattern of gene expression \\u003cstrong\\u003e(\\u003c/strong\\u003eSup Fig.\\u0026nbsp;5). Surprisingly, the relative contribution of each of these clusters to the overall makeup of the macrophage lineage was not changed by loss of Smad3 expression. While there were increased numbers of monocyte-macrophage cells, there was not a significant difference in the relative makeup of the lesion macrophages by these specific subgroups, or difference in overall expression of inflammatory mediators.\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cspan class=\\\"ItalicUnderline\\\" name=\\\"Emphasis\\\" type=\\\"ItalicUnderline\\\"\\u003eSmad3 cooperates with other transcription factors to regulate SMC genes that are involved in human vascular remodeling and disease\\u003c/span\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eGiven that \\u003cem\\u003eSmad3\\u003c/em\\u003e is likely affecting all SMC lineage phenotypes beyond just the CMC and R-SMC populations described above, we characterized the alteration in gene expression of all de-differentiated SMC derived cells in the atherosclerotic lesions. To rigorously identify differentially regulated genes, we employed a Wilcoxon rank sum-based analysis comparing \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e versus control mouse data for FMC plus CMC and R-SMC cellular clusters and 83 genes were identified (Sup. Table\\u0026nbsp;2). Pathway analysis performed using DAVID demonstrated significant enrichment in TGF\\u0026beta; signaling, which is consistent with Smad3\\u0026rsquo;s role in the TGF\\u0026beta; pathway (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA). In addition, biological processes broadly related to ECM remodeling, SMC differentiation, and chemotaxis were also highly enriched. Utilizing GREAT \\u003csup\\u003e48\\u003c/sup\\u003e, we compared the list of 83 identified \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e marker genes to the top 1000 genes expressed by de-differentiated SMC in diseased murine vessels. The analysis revealed enrichment for genes linked to human atherosclerosis and arterial dilatation (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB). In addition to \\u003cem\\u003eMmp3\\u003c/em\\u003e, and consistent with the observed phenotype of increased outward remodeling in \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e mice, \\u003cem\\u003eLox\\u003c/em\\u003e and \\u003cem\\u003eMfap5\\u003c/em\\u003e were also among the top significantly down-regulated genes in de-differentiated \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e SMC (Sup table 2). Pathogenic loss of function in both of these genes has been linked to human vascular syndromes including aortic aneurysms \\u003csup\\u003e49\\u0026minus;51\\u003c/sup\\u003e. To investigate whether these genes are directly regulated by TGF\\u0026beta;, we stimulated human coronary artery smooth muscle cells (HCASMC) with TGF\\u0026beta; in the presence and absence of \\u003cem\\u003eSMAD3\\u003c/em\\u003e. A subset of genes, such as \\u003cem\\u003eLOX\\u003c/em\\u003e, appeared to be directly regulated by TGF\\u0026beta; in HCASMC, and this effect was abrogated by \\u003cem\\u003eSMAD3\\u003c/em\\u003e silencing (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eC), similar to the regulation of \\u003cem\\u003eMMP3\\u003c/em\\u003e. However, a subset of genes, such as \\u003cem\\u003eMFAP5\\u003c/em\\u003e, did not appear to be TGF\\u0026beta; responsive, but were sensitive to loss of \\u003cem\\u003eSMAD3\\u003c/em\\u003e (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eC). These findings suggest involvement of additional co-regulatory factors.\\u003c/p\\u003e\\n \\u003cp\\u003eTo better understand the context in which Smad3 regulates this complex transcriptional program and identify possible co-regulatory factors, we analyzed the 5\\u0026rsquo; regulatory elements of the 83 Smad3 differentially regulated genes to look for enriched transcription factor motifs. Motif analysis conducted with HOMER revealed enrichment for Hox/homeobox motifs as well as Sox9/10 related motifs (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eD). \\u003cem\\u003eHox\\u003c/em\\u003e and \\u003cem\\u003eSox\\u003c/em\\u003e genes were not down-regulated in the \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e SMC (Sup. Figure\\u0026nbsp;6A), suggesting that these factors are likely directly interacting with Smad3 to regulate a joint transcriptional program. Consistent with this hypothesis, Sox9 is known to be a critical transcription factor regulating calcification, and has been shown in chondrosarcoma cells to selectively interact with Smad3, but not Smad2, to modulate an endochondral ossification program \\u003csup\\u003e52\\u003c/sup\\u003e. Since Hoxb2 and Sox9 were found to be coordinately expressed in transition SMC (Suppl. Figure\\u0026nbsp;6B) we further investigated the possible interaction of these factors with Smad3. Knocking down \\u003cem\\u003eSOX9\\u003c/em\\u003e and \\u003cem\\u003eHOXB2\\u003c/em\\u003e, the most highly expressed \\u003cem\\u003eHOX\\u003c/em\\u003e gene in human coronary SMC, recapitulated the changes in expression of key vascular remodeling genes \\u003cem\\u003eMMP3\\u003c/em\\u003e and \\u003cem\\u003eMFAP5\\u003c/em\\u003e observed \\u003cem\\u003ein vivo\\u003c/em\\u003e in mouse (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eE). Thus, we hypothesize that HOX factors, whose motifs are also enriched, can directly interact with SMAD3. To test this hypothesis, we performed nuclear co-immunoprecipitation (co-IP) experiments to test their interaction. In cells over-expressing His-HOXB2 or Flag-SOX9, SMAD3 protein was co-immunoprecipitated with anti- His or Flag antibody respectively (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eF, Sup Fig.\\u0026nbsp;7). Furthermore, proximity ligation assays demonstrated that endogenous SMAD3 is localized in proximity (\\u0026lt;\\u0026thinsp;40nm) to HOXB2 and SOX9 to suggest their involvement in multi-protein complexes in the nucleus of HCASMC (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eG). Taken together, these data suggest HOX family proteins such as HOXB2, as well as SOX family member SOX9, physically interact in the nucleus to regulate transcription of targeted genes.\\u003c/p\\u003e\\n \\u003cp\\u003eTo test the functional significance and epistatic relationship of these findings, we cloned an evolutionarily conserved regulatory region near the 5\\u0026rsquo; end of the human \\u003cem\\u003eMFAP5\\u003c/em\\u003e gene, which contains conserved putative HOX, SOX, and SMAD binding elements, into a luciferase vector and tested its ability to respond to SOX9 and HOXB2 binding. SOX9 and HOXB2 efficiently activated this enhancer element but not the control luciferase vector, further establishing a role in this transcriptional program (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eH, Sup Fig.\\u0026nbsp;6C). Knocking down SMAD3 diminished the SOX9 and HOXB2-dependent activation of the luciferase construct, suggesting interaction with SMAD3 is required for regulation of MFAP5 (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eH).\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eIn these experiments we have studied \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003eΔSMC\\u003c/em\\u003e\\u003c/sup\\u003e mice with scRNAseq, cellular anatomy, and lesion \\u003cem\\u003ein situ\\u003c/em\\u003e studies. Highly correlated results from these different approaches provide a consistent picture of the role of this CAD associated transcription factor in vascular disease processes. Applying scRNAseq at unprecedented read depth and cell number combined with lineage tracing allowed us to observe changes in the number of SMC transition cells and transcriptomic changes that were previously unobtainable. These data identified cell state changes related to loss of \\u003cem\\u003eSmad3\\u003c/em\\u003e expression, such as increased SMC transition to the CMC phenotype, a finding substantiated with increased \\u003cem\\u003eCol2a1\\u003c/em\\u003e plaque cell expression and vascular calcification. Furthermore, the loss of \\u003cem\\u003eSmad3\\u003c/em\\u003e promoted SMC transition to a unique SMC phenotype cell, R-SMC, which have an apparent role in modulating vascular remodeling and macrophage recruitment. Increased expression of cellular proliferation genes by scRNAseq was consistent with the increased number of SMC lineage traced cells in the plaque (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e), echoing previous \\u003cem\\u003ein vitro\\u003c/em\\u003e findings in HCASMC \\u003csup\\u003e29\\u003c/sup\\u003e. Finally, leveraging rich transcriptomic data from a large number of cells, we identified transcriptional interaction of SMAD3 with HOX and SOX factors in the regulation of genes expressed in de-differentiated SMC transition phenotypes.\\u003c/p\\u003e \\u003cp\\u003eThe high resolution scRNAseq data has allowed us to describe a small but distinct subset of SMC transition cells that expresses pro-remodeling enzymes. Mmp3, a metalloprotease required for positive remodeling, is expressed most prominently in the R-SMC that are located in the basal plaque where they appear to migrate into the media towards the adventitia, potentially related to the previously described SMC lineage traced population in the adventitia \\u003csup\\u003e53\\u003c/sup\\u003e. Strikingly, in human coronary arteries, this MMP3 expressing pro-remodeling population resides in the same regions of the artery with observed disruption of the nearby elastic lamina, consistent with a role promoting positive remodeling in human coronary disease. These findings suggest that expansion of this population may be a significant contributing factor to positive remodeling and other adverse features. The importance of this MMP3-expressing SMC transition population in modifying plaque rupture risk may explain the seemingly paradoxical observation that a SNP associated with lower-expression of MMP3 is associated with greater luminal coronary artery stenosis on cardiac catheterization, but the higher-expressing variant is associated with more myocardial infarctions \\u003csup\\u003e54\\u003c/sup\\u003e. These genetic observations provide further evidence that modulation of R-SMC alters plaque features and CAD risk in human. While \\u003cem\\u003eMmp3\\u003c/em\\u003e was previously thought to be expressed by macrophages in the lesions \\u003csup\\u003e54\\u003c/sup\\u003e, we found no evidence that \\u003cem\\u003eMmp3\\u003c/em\\u003e was expressed by cells in the macrophage cluster. Previous work has shown that IL1 drives positive remodeling of plaques and this function was completely reversed by deletion of \\u003cem\\u003eMmp3\\u003c/em\\u003e \\u003csup\\u003e47\\u003c/sup\\u003e. This suggests that SMC are the primary mechanism for the high-risk plaque feature of positive remodeling, as seen in the \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003eΔSMC\\u003c/em\\u003e\\u003c/sup\\u003e mice, and may account in part for the beneficial effect of IL1 blockade on the risk of plaque rupture in humans \\u003csup\\u003e6\\u003c/sup\\u003e. \\u003cem\\u003eMmp3\\u003c/em\\u003e also appears differentially regulated among restricted populations of SMC progeny in carotid artery plaque \\u003csup\\u003e19\\u003c/sup\\u003e, suggesting R-SMC exist in other atherosclerotic beds.\\u003c/p\\u003e \\u003cp\\u003eInterestingly, this population of R-SMC also expresses a number of chemokines, including \\u003cem\\u003eCxcl12\\u003c/em\\u003e, whose main receptor \\u003cem\\u003eCxcr4\\u003c/em\\u003e is expressed solely on the monocyte-macrophage lineage. This suggests that the R-SMC population likely plays a role in regulating the inflammatory response to the lesion, contributing to the increase in observed monocyte-macrophage population detected in the lesions. The combination of remodeling and inflammatory cell recruitment, both factors that determine plaque stability, highlights the critical role that this specific sup-population of cells may play in modulating human disease risk. This contributes to existing literature \\u003csup\\u003e55\\u003c/sup\\u003e suggesting that SMC play a central role in regulating inflammatory cell recruitment and retention in atherosclerotic plaque and identifies a specific sub-population of transition SMC critical for high-risk plaque features.\\u003c/p\\u003e \\u003cp\\u003eThere was also an increase in CMC in \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003eΔSMC\\u003c/em\\u003e\\u003c/sup\\u003e mice suggesting Smad3 actively inhibits differentiation to this phenotype or inhibits their proliferation. The transcriptomic, topological, and lineage inference data presented here indicate that they are a distinct population from \\u003cem\\u003eMmp3\\u003c/em\\u003e-expressing R-SMC. The CMC exhibit a chondrogenic transcriptomic program \\u003csup\\u003e28\\u003c/sup\\u003e, expressing \\u003cem\\u003eCol2a1\\u003c/em\\u003e, \\u003cem\\u003eAcan\\u003c/em\\u003e, and \\u003cem\\u003eSox9\\u003c/em\\u003e, with similarities to chondrogenic progenitors in endochondral bone formation and repair. Smad3 has been shown to regulate Sox factor transcriptional activity in a TGFβ-independent manner through physical interactions \\u003csup\\u003e52,56\\u003c/sup\\u003e. The observed expansion of the CMC also provides an interesting parallel to established findings of accelerated bone and wound healing in \\u003cem\\u003eSmad3\\u003c/em\\u003e knockout mice \\u003csup\\u003e57,58\\u003c/sup\\u003e. The concomitant increase in \\u003cem\\u003eCol2a1\\u003c/em\\u003e expressing cells in the plaque and increased vascular calcification suggests that this cell type is at least partially responsible for coronary calcifications seen in human coronary artery disease. It remains to be determined whether increased calcification is harmful or protective in terms of plaque rupture risk, since conflicting observational data exists in humans. While increased coronary calcification is correlated with increased risk of myocardial infarction \\u003csup\\u003e59\\u003c/sup\\u003e, local calcification appears to be protective against plaque rupture \\u003csup\\u003e60\\u0026minus;62\\u003c/sup\\u003e and interventions that lower risk of plaque rupture increase calcification \\u003csup\\u003e63,64\\u003c/sup\\u003e. Given the multiple populations of SMC-derived transition cells observed in our studies, their relative ratios could possibly determine the quality of calcification as well, which is also considered to confer differential risk of plaque stability\\u003csup\\u003e65,66\\u003c/sup\\u003e .\\u003c/p\\u003e \\u003cp\\u003eRecent work by Chen et al. \\u003csup\\u003e44\\u003c/sup\\u003e has employed single cell studies to investigate the role of Tgfβ signaling in vascular disease, employing a combined Marfan II/ Loeys-Dietz and hypercholesterolemia mouse model. A key finding by these investigators was evidence for an SMC derived mesenchymal stem cell that gives rise to numerous cell types, including adipocytes, osteoblasts/chondrocytes (CMC) and macrophages, in the context of \\u003cem\\u003eTgfrb2\\u003c/em\\u003e knockout and high fat diet. In their study increased plaque inflammation was due in large part to increased SMC-derived macrophage number in the vessel wall through this process. In studies reported here, we did not find evidence for an SMC-derived stem cell that mediates this effect and no evidence that SMC can transition into adipocytes or macrophages in wildtype or \\u003cem\\u003eSmad3\\u003c/em\\u003e KO mice. By contrast, we found that wildtype SMC give rise to fibroblast-like (fibromyocyte) phenotype cells that subsequently give rise to CMC, and with \\u003cem\\u003eSmad3\\u003c/em\\u003e KO the unique cluster of R-SMC derived cells. Interestingly, other published scRNAseq studies have found evidence both for and against SMC-macrophage transition \\u003csup\\u003e19,23,33,44,67\\u003c/sup\\u003e. The observed differences between the \\u003cem\\u003eTgfbr2\\u003c/em\\u003e KO and \\u003cem\\u003eSmad3\\u003c/em\\u003e KO suggest a fundamentally different mechanism by which SMC transition in response to signaling through these two different molecules.\\u003c/p\\u003e \\u003cp\\u003eNumerous other differences are identified with comparison of the \\u003cem\\u003eSmad3\\u003c/em\\u003e and \\u003cem\\u003eTgfbr2\\u003c/em\\u003e knockout models. We do not find evidence for the dramatic increase in lipid accumulation in the \\u003cem\\u003eSmad3\\u003c/em\\u003e mice that was seen in the \\u003cem\\u003eTgfbr2\\u003c/em\\u003e mice. Interestingly, we also did not find increased medial thickness, and the calcification that we identified in the \\u003cem\\u003eSmad3\\u003c/em\\u003e KO mice was in the plaque, not in the media as described for \\u003cem\\u003eTgfbr2\\u003c/em\\u003e. This is an important difference, given the relationship of different types of calcification to disease risk, and consistent with differences in the relative number of CMC generated in each disease model. Our studies showed an increased diameter of the diseased aortic tissue, but without differential loss of medial SMC and no evidence of aneurysm formation as identified in the \\u003cem\\u003eTgfbr2\\u003c/em\\u003e KO model. Regarding MMP genes that likely have a role in aneurysm and remodeling, both \\u003cem\\u003eMmp2\\u003c/em\\u003e and \\u003cem\\u003eMmp3\\u003c/em\\u003e were highly up-regulated in the \\u003cem\\u003eTgfbr2\\u003c/em\\u003e KO but only \\u003cem\\u003eMmp3\\u003c/em\\u003e was upregulated in the \\u003cem\\u003eSmad3\\u003c/em\\u003e KO. Overall, gene ontology analysis with differentially regulated genes in the \\u003cem\\u003eSmad3\\u003c/em\\u003e KO identified primarily atherosclerosis and aneurysm terms while this analysis with \\u003cem\\u003eTgfbr2\\u003c/em\\u003e KO mice identified terms related primarily to vascular calcification.\\u003c/p\\u003e \\u003cp\\u003eThese differences between two \\u003cem\\u003eTGFB\\u003c/em\\u003e pathway molecules are consistent with the known complexity of \\u003cem\\u003eTGRBR2\\u003c/em\\u003e and \\u003cem\\u003eSMAD3\\u003c/em\\u003e signaling. While \\u003cem\\u003eSMAD3\\u003c/em\\u003e was originally identified and characterized in the context of canonical TGFB signaling \\u003csup\\u003e68\\u003c/sup\\u003e, it was also shown that TGFB receptors can signal through SMAD2 as well as other non-SMAD \\u0026ldquo;non-canonical\\u0026rdquo; signaling pathways, including those mediated by mitogen activated protein (MAP) kinases (ERK, p38 and JNK), phosphatidylinositol-3-kinase (PI3K) and RHO-like GTPases in different cell types \\u003csup\\u003e69\\u003c/sup\\u003e. SMAD3 also binds and is regulated by ancillary pathway factors such as FHL3, SKI and ZEB2, with each of these factors in turn regulated by a variety of pathways that can signal independently of TGFB. These fundamental differences between TGFBR2 and SMAD3 signaling are exemplified by their different embryonic phenotypes, with \\u003cem\\u003eTgfbr2\\u003c/em\\u003e knockout embryos undergoing fatality at 10.5 days post conception while \\u003cem\\u003eSmad3\\u003c/em\\u003e knockout mice are born and survive into adulthood. Given these differences in molecular signaling, developmental loss-of-function phenotypes, and striking differences in cellular and molecular single cell analyses in the context of atherosclerosis, we surmise that SMAD3 and TGFBR2 have overlapping but distinct signaling mechanisms, with differential disease related effects on SMC transition phenotypes.\\u003c/p\\u003e \\u003cp\\u003eBeyond the changes in proportions of the different SMC derivatives, loss of \\u003cem\\u003eSmad3\\u003c/em\\u003e also resulted in alterations in SMC transition phenotype transcriptomes as a whole. Gene knockout down-regulated several important ECM genes, including \\u003cem\\u003eLox, Mfap5\\u003c/em\\u003e and \\u003cem\\u003eEln\\u003c/em\\u003e, whose loss of function mutations have been associated with Mendelian aortopathies. These findings suggest that global transcriptomic changes associated with \\u003cem\\u003eSmad3\\u003c/em\\u003e loss weaken the vascular wall and thus further promote positive or outward vascular remodeling. These finding may also have implications in non-atherosclerotic vasculopathies, such as Marfan and Loeys-Dietz syndromes. Aortopathies such as Marfan\\u0026rsquo;s syndrome have been shown to produce aberrant SMC derived populations that contribute to pathogenesis \\u003csup\\u003e70\\u003c/sup\\u003e. In fact, our previous scRNAseq studies of a murine Marfan model also demonstrated an increase in \\u003cem\\u003eMmp3\\u003c/em\\u003e expressing SMC progeny and lower \\u003cem\\u003eMfap5\\u003c/em\\u003e expression, suggesting our findings here may extend beyond atherosclerotic disease.\\u003c/p\\u003e \\u003cp\\u003eHuman genetics data has previously suggested the lead CAD-associated SNP rs56062135 at 15q22 is in linkage disequilibrium (LD) with SNP rs17293632 that appears to promote AP-1 binding and increase \\u003cem\\u003eSMAD3\\u003c/em\\u003e expression in vitro and in vivo \\u003csup\\u003e20\\u003c/sup\\u003e, suggesting higher \\u003cem\\u003eSMAD3\\u003c/em\\u003e expression may be associated with risk of myocardial infarction \\u003csup\\u003e9,20,29\\u003c/sup\\u003e. This is clearly contradictory to our finding that complete loss of \\u003cem\\u003eSmad3\\u003c/em\\u003e increases plaque size in our murine atherosclerosis model, and there are several possible explanations for this disparity. It is possible that alternate SNPs in LD with rs56062135 have the opposite effect on \\u003cem\\u003eSMAD3\\u003c/em\\u003e expression in the context of certain types of cellular stimulation, i.e., serve as response QTLs. In this case, the response QTLs may have a greater effect on \\u003cem\\u003eSMAD3\\u003c/em\\u003e expression and the integrative effect of the entire haploblock on \\u003cem\\u003eSMAD3\\u003c/em\\u003e expression would be opposite and greater than the effect of rs17294632. Alternatively, it is possible that cell-fate changes identified in \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003eΔSMC\\u003c/em\\u003e\\u003c/sup\\u003e mice actually overall stabilize the human lesion and thereby protect it from plaque rupture, despite there being larger lesion size and plaque burden. This paradoxical effect has previously been observed. For example, the IL1β blocking antibody canakinumab decreased the risk of myocardial infarction in human trials but \\u003cem\\u003eIl1r\\u003c/em\\u003e blockade/knockout increased the lumen obstruction and plaque size in mouse models\\u003csup\\u003e47 71 72\\u003c/sup\\u003e. Importantly, \\u003cem\\u003eIl1r1\\u003c/em\\u003e loss drastically changed plaque composition, suggesting SMC cell fate in plaques may be a stronger determinant for plaque rupture than plaque size alone. Indeed, it has been observed that the largest plaques seen on coronary angiogram are usually not the ones that rupture and cause myocardial infarction \\u003csup\\u003e73\\u003c/sup\\u003e. Recent human epidemiological and clinical data also suggest that the quality of calcification is critical, and that some types of more calcified plaques are less likely to rupture \\u003csup\\u003e66\\u003c/sup\\u003e. It is possible that the increased calcification seen in \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003eΔSMC\\u003c/em\\u003e\\u003c/sup\\u003e mice is correlated with lesions in humans that are protected against myocardial infarction, which then contributes to the protective genetic signal. The exact explanation for these seemingly opposite findings holds the key to translating human genetics into better understanding of the pathophysiology and identifying new molecular therapies for atherosclerosis, and will only be discovered by detailed mechanistic studies of additional genetic loci that harbor risk for coronary artery disease.\\u003c/p\\u003e\"},{\"header\":\"Materials And Methods\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eMouse strains\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSMC-specific lineage tracing and \\u003cem\\u003eSmad3\\u003c/em\\u003e knockout was generated by a well-characterized BAC transgene that expresses a tamoxifen-inducible Cre recombinase driven by the SMC-specific \\u003cem\\u003eMyh11\\u003c/em\\u003e promoter (\\u003cem\\u003eTg\\u003c/em\\u003e\\u003csup\\u003eMyh11\\u0026minus;CreERT2\\u003c/sup\\u003e; 019079; JAX). These mice were bred with a floxed-stop-flox \\u003cem\\u003etdTomato\\u003c/em\\u003e fluorescent reporter line (B6.Cg-\\u003cem\\u003eGt(ROSA)26Sor\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003etm14(CAGtdTomato)Hze\\u003c/em\\u003e\\u003c/sup\\u003e/J; 007914; JAX) to allow SMC-specific lineage tracing. \\u003cem\\u003eSmad3\\u003c/em\\u003e conditional knockout were obtained from Matzuk lab from Univ Texas SW \\u003csup\\u003e39,40\\u003c/sup\\u003e with LoxP sites flanking exons 2 and 3 which contains Smad3 DNA binding domain and creates a non-functioning frame-shift mutation after deletion\\u003csup\\u003e41\\u003c/sup\\u003e. All mice were back-crossed onto the C56BL/6 \\u003cem\\u003eApoE\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026minus;/\\u0026minus;\\u003c/em\\u003e\\u003c/sup\\u003e background. As the Cre-expressing BAC was integrated into the Y chromosome, all lineage-tracing mice in the study were male. The animal study protocol was approved by the Administrative Panel on Laboratory Animal Care at Stanford University.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eInduction of lineage marker and\\u003c/strong\\u003e \\u003cspan class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\" type=\\\"BoldItalic\\\"\\u003eSmad3\\u003c/span\\u003e \\u003cstrong\\u003eknockout by Cre recombinase\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFor all experiments, tamoxifen gavage schedule was as follows: two doses of tamoxifen, at 0.2 mg g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e bodyweight, were administered by oral gavage at 7\\u0026ndash;8 weeks of age, with each dose separated by 72\\u0026ndash;96 hrs. HFD was started (101511; Dyets; 21% anhydrous milk fat, 19% casein and 0.15% cholesterol) after the second gavage.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMouse aortic root/ascending aorta cell dissociation\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eImmediately after sacrifice, mice were perfused with phosphate buffered saline (PBS). The aortic root and ascending aorta were excised, up to the level of the brachiocephalic artery. Tissue was washed three times in PBS, placed into an enzymatic dissociation cocktail (2 U ml\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e Liberase (5401127001; Sigma\\u0026ndash;Aldrich) and 2 U ml\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e elastase (LS002279; Worthington) in Hank\\u0026rsquo;s Balanced Salt Solution (HBSS)) and minced. After incubation at 37\\u0026deg;C for 1 h, the cell suspension was strained and then pelleted by centrifugation at 500\\u003cem\\u003eg\\u003c/em\\u003e for 5 min. The enzyme solution was then discarded, and cells were resuspended in fresh HBSS. To increase biological replication, multiple mice were used to obtain single-cell suspensions at each time point. For each scRNA capture, 2 mice were used. 4 separate pairs of isolation were performed for control and \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e, but one control 10X capture unexpectedly failed resulting in a final of 3 captures of control and 4 captures from conditional KO that was included in the analysis. Cells were sorted FACS sorted based off tdTomato expression. \\u003cem\\u003etdT\\u003c/em\\u003e\\u003csup\\u003e+\\u003c/sup\\u003e cells (considered to be of SMC lineage) and \\u003cem\\u003etdT\\u003c/em\\u003e\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e cells were then captured on separate but parallel runs of the same scRNA-Seq workflow (gating strategy and threshold identical to those published in previous work by Wirka et al\\u003csup\\u003e24\\u003c/sup\\u003e), and datasets were later combined for all subsequent analyses.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSingle-cell capture and library preparation and sequencing\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll single-cell capture and library preparation was performed at the Stanford Functional Genomics Facility and Stanford Genomic Sequencing Service Center. Cells were loaded into a 10x Genomics microfluidics chip and encapsulated with barcoded oligo-dT-containing gel beads using the 10x Genomics Chromium controller according to the manufacturer\\u0026rsquo;s instructions. Single-cell libraries were then constructed according to the manufacturer\\u0026rsquo;s instructions (Illumina). Libraries from individual samples were multiplexed into one lane before sequencing on an Illumina platforms with targeted depth of 50,000 reads per cell.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eHuman coronary artery cell section\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eHuman coronary arteries used in this study were dissected from explanted hearts of transplant recipients, and were obtained from the Human Biorepository Tissue Research Bank under the Department of Cardiothoracic Surgery from consenting patients with approval from the Stanford University Institutional Review Board as previously described.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePreparation of mouse aortic root sections\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eImmediately after sacrifice, mice were perfused with 0.4% paraformaldehyde (PFA). The mouse aortic root and proximal ascending aorta, along with the base of the heart, was excised and immersed in 4% PFA at 4\\u0026deg;C for 24 hrs. After passing through a sucrose gradient, tissue was frozen in optimal cutting temperature compound (OCT) to make blocks. Blocks were cut into 7-\\u0026micro;m-thick sections for further analysis.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eImmunohistochemistry\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIHC was performed according to standard protocol. Primary antibodies: Anti-SM22alpha rabbit polyclonal primary antibody (1:300 dilution; ab14106; Abcam), a Mmp3 Rabbit monoclonal antibody (1:200 dilution; Abcam 52915 ) or a CD68 rabbit polyclonal antibody (1:400 dilution; ab125212; Abcam). Secondary: Rabbit-on-Rodent HRP Polymer (RMR622; Biocare Medical). The processed sections were visualized using a Leica DM5500 microscope objective magnifications, and images were obtained using Leica Application Suite X software. Sections obtained at equal distance measured from the superior margin of the aortic sinus were used for comparison. Areas of interest were quantified using ImageJ (National Institutes of Health) software, and compared using a two-sided \\u003cem\\u003et\\u003c/em\\u003e-test. Lesion size was defined by the area encompassing the intimal edge of the lesion to the border of Tagln positive intima-media junction. Area encompassed by the vessel media was defined by area encircled by the outer edge of Tagln staining of vessel media. All area quantification was performed in a genotype blinded fashion with image J using length information embedded in exported files. Von Kossa stain was performed using Abcam 150687 kit with manufacturer\\u0026rsquo;s recommended protocol with 90-minute development time. All biological replicates for each staining were performed simultaneously on position-matched aortic root sections to limit intra-experimental variance. Folded sections that were uninterpretable after processing were removed.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eRNAscope assay\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSlides were processed according to the manufacturer\\u0026rsquo;s instructions, and all reagents were obtained from ACD Bio. In short, slides were washed once in PBS, then immersed in 1\\u0026times; Target Retrieval reagent at 100\\u0026deg;C for 5 min. Slides were washed twice in deionized water, immersed in 100% ethanol and air dried, and sections were encircled with a liquid-blocking pen. Sections were incubated with Protease III reagent for 30 min at 40\\u0026deg;C, then washed twice with deionized water. Sections were incubated with commercially available probes against mouse \\u003cem\\u003eMmp3\\u003c/em\\u003e, \\u003cem\\u003eCol2a1\\u003c/em\\u003e, and human \\u003cem\\u003eMMP3\\u003c/em\\u003e or a negative control probe for 2 hrs at 40\\u0026deg;C. Colorimetric assays were performed per the manufacturer\\u0026rsquo;s instructions.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAnalysis of scRNA-Seq data\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFastq files from each experimental time point and mouse genotype were aligned to the reference genome (mm10) individually using CellRanger Software (10x Genomics). Individual datasets were aggregated using the CellRanger aggr command without subsampling normalization. The aggregated dataset was then analyzed using the R package Seurat \\u003csup\\u003e74\\u003c/sup\\u003e. The dataset was trimmed of cells expressing fewer than 750 genes, and genes expressed in fewer than 50 cells. The number of genes, number of unique molecular identifiers and percentage of mitochondrial genes were examined to identify outliers. As an unusually high number of genes can result from a \\u0026lsquo;doublet\\u0026rsquo; event, in which two different cell types are captured together with the same barcoded bead, cells with \\u0026gt;\\u0026thinsp;6000 genes were discarded. Cells containing\\u0026thinsp;\\u0026gt;\\u0026thinsp;7.5% mitochondrial genes were presumed to be of poor quality and were also discarded. The gene expression values then underwent library-size normalization and normalized using established Single-Cell-Transform function in Seurat. Principal component analysis was used for dimensionality reduction, followed by clustering in principal component analysis space using a graph-based clustering approach via Louvain algorithm. UMAP was then used for two-dimensional visualization of the resulting clusters. Lineage inference was performed using Slingshot with available Slingshot software in R using converted Seurat object into singlecellexperiment objects. Analysis, visualization and quantification of gene expression and generation of gene module scores were performed using Seurat\\u0026rsquo;s built-in function such as \\u0026ldquo;FeaturePlot\\u0026rdquo;, \\u0026ldquo;VlnPlot\\u0026rdquo;, \\u0026ldquo;AddModuleScore\\u0026rdquo;, and \\u0026ldquo;FindMarker.\\u0026rdquo; Lists of genes associated with each GO category were obtained from Geneontology.org. Panther / DAVID / GO / GREAT analysis was performed using web-based platform at Geneontology.org, Great.Stanford.Edu, and David.ncifcrf.gov. Top 1000 genes expressed in modulated SMC was defined by the highest expressing 1000 transcripts (based on average expression) from scRNA data in all de-differentiated lineage traced cells. Promoter/5\\u0026rsquo;-Regulatory region of genes were extracted utilizing UCSC table browser based off 1kb upstream of TSS of transcripts. Motif analysis was performed using freely available HOMER software \\u003csup\\u003e75\\u003c/sup\\u003e with findMotifGenome function. The regulatory region of the top 1000 gene was used as the background as bases for motif enrichment.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eHCASMC culture/experiments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eCells were cultured in smooth muscle growth medium (Lonza; catalog number: CC-3182) supplemented with human epidermal growth factor, insulin, human basic fibroblast growth factor and 5% FBS, according to the manufacturer\\u0026rsquo;s instructions. All HCASMC lines were used at passages 4\\u0026ndash;8. siRNA knockdowns were performed using Lipofectamine RNAiMax (Life Technologies) using manufacturer\\u0026rsquo;s recommended protocol at 50pg siRNA / 100,000 cells. Cells were allowed to recover in SMC growth medium (with or without additional growth factor) for 36 hours prior to RNA harvest. Recombinant TGFB (PeproTech 100\\u0026thinsp;\\u0026minus;\\u0026thinsp;21) concentration used in stimulation was 10ng/ml.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eProximity Ligation Assay\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eHuman coronary artery smooth muscle cells were cultured on tissue-culture slides in serum containing media for 24 hours. The cells were then fixed with 4% PFA for 30 minutes at room temperature. Proximity ligation assays were performed on these slides using a Sigma DuoLINK kit (DUO92101) with rabbit anti-SMAD3 antibody (Cell Signaling 9523S (1:200)), mouse anti-HOXB2 monoclonal antibody (DSHB: PCRP-HOXB2-1C9 (1:50 (hybridoma supernatant)), or mouse anti-SOX9 monoclonal antibody (DSHB PCRP-SOX9-1A2 (1:50 hybridoma supernatant)).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCo-IP Experiment\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMyc-Flag-tagged SOX9 (PS100016 Origene) and 6x-HIS-tagged-HOXB2 (Addgene 8522) were obtained from commercial vendors and cloned into pCMV6 vector and transfected into HEK cells. The cells were allowed to recover for 36 hours after media change and Nuclear-Complex Co-IP was performed using commercially available Nuclear-Complex Co-IP kit from ActiveMotif(54001) with manufacturer\\u0026rsquo;s recommended protocol using mouse Anti-Flag (Sigma F3165) or mouse Anti-His (Abcam 18184) antibody for immunoprecipitation, followed by blotting using Rabbit anti-Smad3 antibody (Cell Signaling 9523S), followed by Anti-Rabbit HRP (Cell Signaling 7074S) and detected via Luminata Forte Western HRP substrate (Millipore).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLuciferase experiments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFor Luciferase experiments, an evolutionary conserved region of human \\u003cem\\u003eMFAP5\\u003c/em\\u003e regulatory region (chr12:8815212\\u0026ndash;8815569) was cloned from human genomic DNA and placed into pLuc-MCS vector, whereas inert/scramble similar length spacer was cloned into baseline pLuc-MCS as control. pCMV6-empty, and cloned pCMV6-Flag-SOX9 or pCMV6-his-HOXB2 were transfected into cells via lipofectamine 2000 along with respective luciferase and Renilla vector. Media was changed after 6 h, and dual-luciferase activity (Promega) was recorded after 24 h using a SpectraMax L luminometer (Molecular Devices). Relative luciferase activity (firefly/\\u003cem\\u003eRenilla\\u003c/em\\u003e luciferase ratio) is expressed as the fold change over control conditions.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMmp3 Activity Assay\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMmp3 activity was measured via Abcam MMP-3 Activity Assay Kit (ab118972) following their tissue-based activity measurement protocol. 0.5 cm of dissected thoracic aorta from identical locations of control and \\u003cem\\u003eSmad3\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026Delta;SMC\\u003c/em\\u003e\\u003c/sup\\u003e mice were placed in the tissue homogenizer for 10 seconds on ice in chilled assay buffer. After centrifugation, the supernatants were then directedly assayed. Mmp3 activity was measured at 10 minutes and 30 minutes after initiation of the reaction, using a SpectraMax luminometer at Ex/Em\\u0026thinsp;=\\u0026thinsp;325/393 nm, with exposure of 600ms. Two biological replicates with 3 separate segments of thoracic aorta were used for the assay.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTranswell Assay\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eHCASMCs were grown in 24 well plates at low density at 10,000cells/well. SMAD3 knockdown was performed 24 hours prior to initiation of the migration experiment. HCASMCs were washed with PBS and cultured in serum free HCASMC media. 8um cell culture inserts (Corning 353097) were placed into the wells. THP-1 cells (ATCC) were grown in standard culture conditions. then spun down, and resuspended in serum-free HCASMC media, and placed in the top chamber for 3 hours at 37C. After 3 hours, THP1 cells in the bottom chamber in suspension were quantified.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTUNEL Assay\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTUNEL assay was performed on cryopreserved aortic root sections using commercially available chromogenic TUNEL assay kits. Quantification was performed on 40X magnification at one random point on each cusp and TUNEL\\u0026thinsp;+\\u0026thinsp;nuclei was counted manually in a blinded manner.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eStatistical methods\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eDifferentially expressed genes in the scRNA-Seq data were identified using a Wilcoxon rank-sum test. Distribution of cells within defined-populations was tested via X-square test. Significance determination of histological measurement, luciferase studies, qPCR results, and composite gene-score were done via two-tailed T-test. Multiple comparisons were corrected via Bonferroni correction when necessary.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSpecial thanks to the Yana Ryan, Krista Hennig, Peter Mcguire and Hassan Chaib at the Stanford Genomic Sequencing and Service Center (GSSC) for performing 10x capture, library construction, and sequencing. We also thank the Stanford shared FACS facility for required FACS analysis and experiments. Also, thanks to the Matzuk lab for providing us with conditional Smad3 knockout mice. Illustrations were made with BioRender software. David Dichek, Univ. Washington, is acknowledged for advice regarding data interpretation.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by National Institutes of Health grants F32HL143847 (PC), K08HL153798 (PC), K08HL152308 (RW), K08HL133375 (JBK), F32HL154681 (AP), R01AR066629 (MF), R01HL109512 (TQ), R01HL134817 (TQ), R33HL120757 (TQ), R01HL139478 (TQ), R01HL156846 (TQ), R01HL151535 (TQ), R01HL145708 (TQ), as well as a Human Cell Atlas grant from the Chan Zuckerberg Foundation. This work was also supported by American Heart Association grant 20CDA35310303 (PC) and 18CDA34110206 (RW).\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors have no competing interests to declare.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;Author Contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePC/TQ:\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003eDesigning research studies, conducting experiments, acquiring data, analyzing data, providing reagents, and writing the manuscript. RW/JK/TN/RK: conduction experiments and acquiring data. QZ/AP/DS/DI/MF: analyzing data and other critical scientific input.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSpecial thanks to the Yana Ryan, Krista Hennig, Peter Mcguire and Hassan Chaib at the Stanford Genomic Sequencing and Service Center (GSSC) for performing 10x capture, library construction, and sequencing. We also thank the Stanford shared FACS facility for required FACS analysis and experiments. Also, thanks to the Matzuk lab for providing us with conditional Smad3 knockout mice. Illustrations were made with BioRender software. David Dichek, Univ. Washington, is acknowledged for advice regarding data interpretation. \\u003cstrong\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by National Institutes of Health grants F32HL143847 (PC), K08HL153798 (PC), K08HL152308 (RW), K08HL133375 (JBK), F32HL154681 (AP), R01AR066629 (MF), R01HL109512 (TQ), R01HL134817 (TQ), R33HL120757 (TQ), R01HL139478 (TQ), R01HL156846 (TQ), R01HL151535 (TQ), R01HL145708 (TQ), as well as a Human Cell Atlas grant from the Chan Zuckerberg Foundation. 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A. \\u003cem\\u003eet al.\\u003c/em\\u003e Angiographic progression of coronary artery disease and the development of myocardial infarction. \\u003cem\\u003eJ Am Coll Cardiol\\u003c/em\\u003e \\u003cstrong\\u003e12\\u003c/strong\\u003e, 56\\u0026ndash;62, doi:\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/0735-1097(88)90356-7\\u003c/span\\u003e\\u003c/span\\u003e (1988).\\u003c/p\\u003e\\n\\u003cp\\u003e74 Stuart, T. \\u003cem\\u003eet al.\\u003c/em\\u003e Comprehensive Integration of Single-Cell Data. \\u003cem\\u003eCell\\u003c/em\\u003e \\u003cstrong\\u003e177\\u003c/strong\\u003e, 1888\\u0026ndash;1902 e1821, doi:\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.cell.2019.05.031\\u003c/span\\u003e\\u003c/span\\u003e (2019).\\u003c/p\\u003e\\n\\u003cp\\u003e75 Heinz, S. \\u003cem\\u003eet al.\\u003c/em\\u003e Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. \\u003cem\\u003eMol Cell\\u003c/em\\u003e \\u003cstrong\\u003e38\\u003c/strong\\u003e, 576\\u0026ndash;589, doi:\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.molcel.2010.05.004\\u003c/span\\u003e\\u003c/span\\u003e (2010)\\u003c/p\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"nature-portfolio\",\"isNatureJournal\":true,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Nature Portfolio\",\"twitterHandle\":\"\",\"acdcEnabled\":false,\"dfaEnabled\":false,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"atherosclerotic plaques, Smad3, smooth muscle cells (SMC)\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-708882/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-708882/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eAtherosclerotic plaques consist mostly of smooth muscle cells (SMC), and genes that influence SMC biology can modulate coronary artery disease (CAD) risk. Allelic variation at 15q22.33 has been identified by genome-wide association studies to modify the risk of CAD, and is associated with expression of \\u003cem\\u003eSMAD3\\u003c/em\\u003e in SMC, but the mechanism by which this gene modifies CAD risk remains poorly understood. SMC-specific deletion of \\u003cem\\u003eSmad3\\u003c/em\\u003e in a murine atherosclerosis model resulted in greater plaque burden, more positive remodeling, and increased vascular calcification. Single-cell transcriptomic analyses revealed that loss of \\u003cem\\u003eSmad3\\u003c/em\\u003e altered SMC transition cell state toward two fates: a novel SMC phenotype that governs both vascular remodeling and recruitment of inflammatory cells, as well as a chondromyocyte fate. The remodeling population was marked by uniquely high \\u003cem\\u003eMmp3\\u003c/em\\u003e and \\u003cem\\u003eCxcl12\\u003c/em\\u003e expression, and its appearance correlated with higher risk plaque features such as increased positive remodeling and macrophage content. Further, investigation of transcriptional mechanisms by which Smad3 alters SMC cell fate revealed novel roles for Hox and Sox transcription factors whose direct interaction with Smad3 regulate an extensive transcriptional program balancing remodeling and vascular extracellular matrix with significant implications for atherosclerotic and Mendelian aortic aneurysmal diseases. Together, these data suggest that \\u003cem\\u003eSmad3\\u003c/em\\u003e expression in SMC inhibits the emergence of specific SMC phenotypic transition cells that mediate adverse plaque features, including positive remodeling, monocyte recruitment, and vascular calcification.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Smad3 Regulates Smooth Muscle Cell Fate and Governs Adverse Remodeling and Calcification of Atherosclerotic Plaque\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2021-07-19 16:16:42\",\"doi\":\"10.21203/rs.3.rs-708882/v1\",\"editorialEvents\":[],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"nature-cardiovascular-research\",\"isNatureJournal\":true,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"natcardiovascres\",\"sideBox\":\"Learn more about [Nature Cardiovascular Research](https://www.nature.com/natcardiovascres/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://mts-natcardiovascres.nature.com/cgi-bin/main.plex\",\"title\":\"Nature Cardiovascular Research\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"Nature Research\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"bf6c2047-689f-4650-9057-6e97f2885087\",\"owner\":[],\"postedDate\":\"July 19th, 2021\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[{\"id\":5799395,\"name\":\"Cardiac \\u0026 Cardiovascular Systems\"}],\"tags\":[],\"updatedAt\":\"2022-04-14T09:50:47+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-708882\",\"link\":\"https://doi.org/10.1038/s44161-022-00042-8\",\"journal\":{\"identity\":\"nature-cardiovascular-research\",\"isVorOnly\":false,\"title\":\"Nature Cardiovascular Research\"},\"publishedOn\":\"2022-04-13 04:00:00\",\"publishedOnDateReadable\":\"April 13th, 2022\"},\"versionCreatedAt\":\"2021-07-19 16:16:42\",\"video\":\"\",\"vorDoi\":\"10.1038/s44161-022-00042-8\",\"vorDoiUrl\":\"https://doi.org/10.1038/s44161-022-00042-8\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-708882\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-708882\",\"identity\":\"rs-708882\",\"version\":[\"v1\"]},\"buildId\":\"FbvkV6FR0MCFSLy54lSbu\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}