Full text

Turn on search term navigation

1. Introduction

Cardiovascular disease (CVD) is the leading cause of death in the United States and after decades of persistent decline, incidence has taken a sharp upturn in recent years [1]. Previous work has identified sex-based differences in prevalence in most cardiovascular disease phenotypes, including coronary artery disease [2,3], stroke and carotid stenosis [4], heart failure [5], and aortic diseases [6,7,8]. Additionally, sexual dimorphism has been observed in the characteristics of atherosclerotic plaques [9]. While possible causes of sex differences in cardiovascular disease have been identified, treatments and protocols that account for sex-specific biology are currently limited. There are lower rates of interventional procedures and less aggressive treatment strategies in women than in men [10], demonstrating the need for sex-specific CVD treatment approaches. Age, hypertension, total cholesterol, and low-density lipoprotein cholesterol may more strongly influence CVD risk in men while smoking, diabetes, triglyceride, and high-density lipoprotein cholesterol levels more strongly influence women [11]. Women-specific factors include sex hormone dysfunction and pregnancy complications [12]. The effects of estrogen on incidence of cardiovascular disease as well as identification of possible mechanisms (disruption of nitric oxide synthesis, dysregulation of lipid profiles, and upregulation of nuclear factor of activated T cells) has been explored [13], but the role of chromosomal complement on sex differences and relevant molecular mechanisms in CVDs still remains largely understudied.

Proteomics studies that identified sex-related differences relevant to CVDs have been conducted on biological fluids, including plasma and serum [14,15]. In plasma, sex differences related to markers of inflammation, lipoprotein metabolism, adipocyte metabolism, calcification and thrombosis were observed [15]. Studies have also been conducted on a variety of animal myocardial tissues [16,17] and human atherosclerotic plaque [18,19]. Proteomic analysis of atherosclerotic carotid plaque samples attributed calcification signatures as well as sex differences (proteoglycans were more abundant in females) to smooth muscle cells [19]. Myosin RLC-9, expressed within vascular smooth muscle cells (SMCs), was reduced within pathological regions across both genders [18]. Sexual dimorphism has also been identified in oxidative stress and oxidative post-translational modifications (PTMs) [20,21,22,23,24]. Oxidative stress will induce endothelial dysfunction and has been implicated in atherosclerosis pathophysiology [25,26,27], and several risk factors for CVD (obesity and smoking, hypertension, and metabolic syndrome) are linked to oxidative stress [28]. Markers of oxidative stress vary with sex and age, and higher serum hydroperoxide levels have been observed in female coronary artery disease patients than male patients, suggesting that estimations of oxidative stress could serve as an indicator of CVD risk [21,23]. Sex differences have also been identified in the metabolome using lipidomics: women generally have higher concentrations of phosphatidylcholines, fatty acids, and sphingomyelins, and lower concentrations of lysophospholipids and ceramindes compared to men. The mechanisms mediating sex differences in lipidomes are also mostly unclear [29]. Therefore, while the existence of sex differences in proteomic and lipidomic profiles have been identified, further insights into the mechanisms contributing to these differences and their biological and clinical relevance is necessary.

Current models may have limited utility in exploring sex differences in CVD and identifying relevant molecular mechanisms, as animal models have limited throughput and variable translation to human. Stem cell-derived models with human cells provide a patient-specific system in which to identify CVD biomarkers and molecular mechanisms. While the proteomic similarity of induced pluripotent stem cell (iPSC)-derived vascular smooth muscle cells to native smooth muscle cells has been examined, our knowledge of the effects of sex on differentiation efficiency of iPSCs to vascular cell types is limited [30]. Existing work demonstrates that extracellular matrix protein expression and proliferation of iPSC-derived smooth muscle cells are affected by cell sex [31], supporting the importance of a more thorough understanding of the impact of chromosomal sex on iPSC-derived smooth muscle cell differentiation, proteome, and function. Previous work has demonstrated that iPSC-derived smooth muscle cell models have a cellular phenotype relevant to smooth muscle cell plasticity and contain the proteomic profiles necessary to examine pathways relevant to arterial diseases [32], and iPSC-derived vascular smooth muscle cells have been used to create an in vitro model of vascular calcification [33]. Also, the role of lineage in smooth muscle cell differentiation has been explored and used to identify functional defects and explore the pathological role of integrin αV in aneurysm formation [33,34]. Therefore, while pathological functionality and molecular pathways have been explored using iPSC-derived vascular smooth muscle cells, sexual dimorphism has not.

Here, we differentiated and characterized four male and four female iPSC lines from healthy human donors and conducted a total proteome analysis on iPSCs and iPSC-derived vascular smooth muscle cells to identify sex-based proteomic differences and highlight their possible effects on cardiovascular pathophysiology. We identified that sex-linked expression of proteins occurs on both sex chromosomes and autosomal proteins, potentially contributing to sex-specific risk of vascular disease.

2. Results

2.1. Characterization of the Differentiation of iPSCs into Vascular Smooth Muscle Cells

In order to examine the efficacy of differentiation, multiple stem cell lines of both sexes (4 male and 4 female lines) were differentiated into vascular smooth muscle cells using an established protocol [35]. Following differentiation, iVSMCs of both sexes expressed representative contractility markers (alpha smooth muscle actin [ACTA2], calponin [CNN1], and myosin heavy chain 11 [MHC11]) and exhibited similar contractile responses to carbachol (Figure 1A,B). Principal component analysis comparing iPSCs, iVSMCs, and primary aortic smooth muscle cells demonstrated that each cell type could be distinguished by their proteome when running Principal Component 1 (PC1) versus Principal Component 2 (PC2). It is interesting to note that iVSMC and primary aortic smooth muscle cell clusters are closer along PC1, and iVSMC and iPSC clusters are closer along PC2. Because PC1 reveals the most variation, and PC2 reveals the second most variation, this demonstrates that the iVSMCs and primary cells have less variation in proteome than the iVSMCs and iPSCs (Figure 1C). We identified a total of 6120 proteins across all 3 cell types, with 5206, 4919, and 4611 proteins identified in iPSCs, iVSMCs, and primary aortic smooth muscle cells, respectively. While the largest proportion of proteins were shared between all three cell types (3798), 37.9% of these proteins showed statistically significant differences in expression level between at least two groups (DEPs). Notably, iVSMCs have 77.1% shared proteins with primary aortic smooth muscle cells and 73.3% shared proteins with iPSCs; we also observed primary aortic smooth muscle cells have a lower number of proteins shared with iPSCs (68.3%) (Figure 1D). The differential expression of proteins unique to vascular smooth muscle cell types and iPSCs can be examined using the volcano plot in Figure 1E. Proteins related to extracellular matrix (COL1A1, COL6A1, and FN1), cell contractile activity (MYL9), and cell proliferation and metabolic activity (IGFBP-3) were upregulated in vascular smooth muscle cell types [36,37,38]. IGFBP-3 has been shown to have an inverse association with the risk of coronary events [39,40]. GATA4, which plays a critical role in differentiation, growth, and survival of many cell types [41], and EVI5, an oncogene that regulates cell cycle and cytokinesis [42], were upregulated in iPSCs. Overall, comparison of proteomic differences between iPSCs and vascular smooth muscle cell types demonstrated expected results, showcasing proteins and pathways indicative of a pluripotent state in iPSCs. This data indicates that iVSMCs are more similar to primary aortic smooth muscle cells than iPSCs on the level of expressed proteome, containing similarities in key proteins not present in iPSCs. This demonstrates the success of the differentiation process in producing cells that exhibit the proteomic and functional characteristics of vascular smooth muscle cells.

Pathway analysis of proteins upregulated in iPSCs and those upregulated in iVSMCs was conducted to examine pathways directly affected by differentiation (Figure 2). Proteins related to cell cycle process, nuclear DNA replication, and RNA helicase activity were upregulated in iPSCs. Existing work suggests cell cycle proteins may contribute to the maintenance of pluripotency [43,44,45], and defects in DNA replication have been shown to contribute to reduced differentiation potential in reprogrammed pluripotent stem cells [46]. RNA helicases, specifically DDX6 and eIF4A3, have both been shown to regulate pluripotency [47,48]. Proteins related to focal adhesions, cadherin binding, and carbohydrate-derivative metabolic process were upregulated in iVSMCs. ITGFB1, ITGAV, ANXA1, and ANXA2 are upregulated in iVSMCs, but, interestingly, ITGA6 was upregulated in iPSCs. The full results of the pathway analysis are included in Supplementary Table S2. This pathway analysis identified pathways of interest in both iPSCs and iVSMCs and demonstrated that differentiation produces cells enriched in pathways that are relevant to vascular smooth muscle cell function.

2.2. Identification of Potential Targets for Improvement in iVSMCs for Modeling Cardiovascular Disease

Analysis of proteomic differences between iVSMCs and human primary aortic smooth muscle cells (ATCC) was conducted in order to identify functional differences or potential targets for further maturation. This comparison found 1762 differentially expressed proteins (Figure 3A), with comparative pathway analysis revealing multiple differential functional pathways between the cell types (Supplemental Table S3), including differences in inflammatory pathways and pathways related to cell–cell and adherens junctions (Figure 3B). In primary aortic smooth muscle cells, proteins related to phenotypic switching (STAT1 and PKM) [49,50] and a regulator of vascular tone (PTGIS) [51] were highly upregulated, and a protein detected in VSMCs of atherosclerotic lesions (KRT8) was upregulated in iVSMCs [52]. Trends in KRT8, STAT1, and PTGIS1 observed in proteomic analysis were confirmed and quantified using Western blots. Thus, while pathway analysis of vascular smooth muscle cell types versus iPSCs demonstrated that iVSMCs and primary aortic smooth muscle cells exhibit proteomic similarities important to vascular smooth muscle cell function, further comparison of iVSMCs and primary aortic smooth muscle cells identifies differences in proteomic targets and pathways relevant to vascular pathophysiology.

2.3. Identification of Proteomic Sex Differences in iPSCs and iVSMCs

A comparison of male and female iPSCs and male and female iVSMCs was conducted to identify sexually dimorphic proteins and examine the role of sex chromosomes in protein expression. A total of 5296 proteins (544 of which were statistically significant) were differentially expressed by sex in iPSCs. Y-linked protein DDX3Y was only found in male iPSCs, as expected. Interestingly, PTGR2, which is involved in the metabolism of prostaglandin E2, was upregulated in female iPSCs, suggesting there may be genetic sex-based differences in eicosanoid production [53] (Figure 4A). Trends in PTGR2 were confirmed via Western blot (Figure 4D,F). ACTB could not be used as a loading control for the iPSCs because it was differential by sex, and so total protein was used as the loading control. In iVSMCs, 5119 proteins (including 444 statistically significant proteins) were differentially expressed by sex. Y-linked proteins DDX3Y and RPS4Y1 were only found in male iVSMCs. Several proteins linked to increased risk of cardiovascular disease were upregulated in male iVSMCS (PCK2, CD109, and IGFBP2) [54,55,56] (Figure 4B). Trends in IGFBP2 were confirmed via Western blot (Figure 4E,G). Among the 164 sex-specific proteins upregulated in male iPSCs and the 243 sex-specific proteins upregulated in male iVSMCs, only 12 proteins were shared. Similarly, out of the 392 proteins upregulated in female iPSCs and 213 proteins upregulated in female iVSMCs, only 32 proteins were shared between both groups (Figure 4C). In terms of relating these sex-biased proteins to cardiovascular disease risk, among the proteins shared between both groups, only a limited number have been previously linked to vascular disease in vascular smooth muscle cells, and these (PUDP and LYRM7) were only upregulated in females (6.25%). Interestingly, also among the sex-biased proteins shared between iPSCs and iVSMCs, 25% of proteins upregulated in males and 31.25% of proteins upregulated in females have been identified as biomarkers of cardiovascular disease in studies utilizing other systems. Interestingly, 8.3% of proteins upregulated in male iVSMCs and 12.5% of proteins upregulated in female iVSMCs have been connected to increased risk of coronary artery disease or acute coronary syndrome in studies utilizing other systems. This demonstrates that there are strongly sex-linked genes with relevance to cardiovascular disease that are not affected by development (Supplementary Tables S4 and S5). Additionally, 53 of proteins upregulated in female iVSMCs were related to increased risk of cardiovascular disease, while 22 of proteins upregulated in male iVSMCs were related to increased risk of cardiovascular disease. Interestingly, more proteins related to increased risk of coronary artery disease were upregulated in female iVSMCs than male iVSMCs. Upon closer examination of the literature associated with these proteins, we inferred that female iVSMCs contained more protective proteins than male iVSMCs, including higher numbers of proteins associated with coronary artery disease, atherosclerosis, and aneurysm (Table 1, Supplementary Tables S6 and S7). It is interesting to note that after differentiation, IGFBP2 changes from being upregulated in females to being upregulated in males—high IGFBP2 levels are associated with the development of major adverse cardiovascular events following acute coronary syndrome [57]. Overall, these results demonstrate that chromosomal sex affects regulation of proteins in both iPSCs and iVSMCs, including ones relevant to cardiovascular disease. Furthermore, development, at least in terms of differentiation into VSMCs, causes sex-specific changes that may increase risk of vascular disease.

Individual analysis of proteomic differences highlights selected key players; however, integration of the data into functional pathways could provide more clear insights into how proteomic differences may impact cell phenotypes. To this end, pathway analysis of proteins regulated by sex in iPSCs (Figure 5A) and in iVSMCs (Figure 5B) was conducted to identify sexually dimorphic pathways. Proteins related to mitochondrion and ATP binding were upregulated in female iPSCs, and proteins related to ATP binding, glycogen metabolic process, and cadherin binding were upregulated in male iVSMCs. It is interesting to note that TOMM20 is highly upregulated in female iPSCs, contradicting the general trend of upregulation of mitochondrial proteins in male iPSCs (the Western blots confirming these results are included in Figure 4D,F). This may indicate changes in the internal composition of mitochondria in males, versus total mitochondrial numbers in females, as TOMM20 is a mitochondrial membrane protein which can be used to quantify mitochondrial number overall. Additionally, ATP binding, glycogen accumulation, and cadherin binding, which are all upregulated pathways in male cells, have been previously linked to various vascular disease-related pathways [58,59,60]. Therefore, sex-specific regulation of the proteins within these pathways may contribute to underlying sex-based differences in cardiovascular disease risk. Also, DCLK1 (which is connected to the ATP binding pathway [Figure 5B]) is strongly upregulated in female iVSMCs, opposing a general trend in upregulation of proteins related to ATP binding in male iVSMCs. DCLK1, EGFR, and MTOR (all connected to the ATP binding pathway [Figure 5B]) have been identified as potential therapeutic targets for atherosclerosis [61,62,63]. Western blots confirm the trends identified by proteomics for EGFR (Figure 4E,G). The full results of the pathway analysis are included in Supplementary Tables S8 and S9. To further examine trends of sexual dimorphism in metabolism, we conducted a targeted assay to examine central carbon chain metabolites [64]. Sex-specific metabolite expression is different in iPSCs vs. iVSMCs,

Demonstrating that differentiation has sex-based effects in metabolite production (Figure 6). Interestingly, all the differentially expressed metabolites that were upregulated in female iVSMCs have been linked to cardiovascular diseases, including atherosclerosis and coronary artery disease [65,66,67,68,69,70,71]. Taken together, this analysis demonstrates that differentiation causes sex-specific changes in the proteome linking metabolic pathway regulation that may contribute to sex-based differences in cardiovascular disease risk, including through sexually dimorphism in metabolism.

Since iPSCs are essentially epigenetically naïve and our cell culture conditions were carefully designed to eliminate hormone and hormone-mimetic agents, we can assume that most if not all proteomic differences between male and female cells are due to chromosomal complement of XX vs. XY. To gain more insights into the role of sex chromosomes in sex-specific proteomic regulation that may lead to cardiovascular disease, we examined the chromosomal location of differentially expressed proteins between our male and female cell lines. As may be expected, in both iPSCs and iVSMCs, more x-linked proteins were upregulated in females than in males (Figure 7A). For iVSMCs, relevance to cardiovascular disease (including increased risk of coronary artery disease, atherosclerosis, and aneurysm or aortic dissection) has been established in 10 of these x-linked proteins upregulated in females and 1 protein upregulated in males (Figure 7A, Table 2, Supplementary Table S10). Interestingly, we also identified that proteins on autosomal chromosomes demonstrate sex-based regulation, including in proteins relevant to cardiovascular pathophysiology and disease (TOMM20, PTGR2, IGFBP2, SULTE1). TOMM20 levels can signal changes in mitochondrial function relevant to increased risk of coronary heart disease in diabetic patients [72], and PTGR2 is involved in the metabolism of prostaglandin E2, which is involved in vasoconstriction [53,73]. Circulating IGFBP2 is a positive predictor of major adverse cardiovascular events [57] and SULT1E1 is upregulated in human atherosclerotic plaques [74] (Figure 7B–G). Line-by-line variability in these proteins can be examined in Supplementary Figure S1, and Western blots of TOMM20, PTGR2, and IGFBP2 are included in Figure 4D–G. In summary, sex chromosomes significantly and broadly alter autosomal gene expression and cardiovascular disease risk, and close examination of these regulatory networks can provide clarity on the role of sex chromosomes in driving proteomic changes related to increased cardiovascular disease risk.

3. Discussion

3.1. iPSC-Derived Vascular Smooth Muscle Cells Can Model Cardiovascular Disease Risk

In the present study, we differentiated and characterized four male and four female healthy iPSC lines and conducted proteomic analysis to examine the similarity of differentiated cells to native aortic smooth muscle cells and identify sex-based proteomic differences that are relevant to cardiovascular pathophysiology. While proteomic phenotyping of iPSC-derived smooth muscle cells has been reported [30,32], sex differences in iPSC-derived vascular smooth muscle cells have not been previously examined. A large degree of heterogeneity can occur despite the use of the same differentiation protocol—while previous work used 1–3 iPSC lines (1 male and 2 females), this work was not sufficiently powered to examine sex differences and using 8 iPSC lines allows us to more strongly account for donor-based variability. Furthermore, while some sex-based biomarkers of cardiovascular disease [9] or sex-stratified gene regulatory networks [75] have been identified, previous work has not examined sex-based differences in biomarkers of cardiovascular disease or examined sex-based expression of proteins on autosomal and sex-linked chromosomes. This investigation demonstrates the utility of iPSC-derived vascular cells in modeling cardiovascular diseases with sex-based risk and may be used in the future to gain clarity regarding the mechanisms driving sex-based risk of cardiovascular disease, as well as to examine the role of sex chromosomes and sex hormones in driving cardiovascular disease risk.

The differentiation protocol produced cells that exhibited important contractile markers (ACTA2, CNN1, and MHC11) and demonstrated a contractile response across all eight lines, as expected for vascular smooth muscle cells. In the future, MHC11 expression could be improved by adding RepSox to the differentiation [32]. While principal component analysis demonstrated that the overall proteomes of the three cell types were clearly different, there were no obvious sex-based differences in the overall proteomes. Additionally, the primary aortic smooth muscle cells shared a higher number of proteins with iVSMCs than iPSCs. This demonstrates that the differentiation was not significantly confounded by donor variability. While this work has addressed inter-donor and intra-clonal variability (differences between batches from the same clone), interclonal variability (differences between clones from the same donor) has not been examined and warrants examination in the future. Examination of the differential expression of proteins unique to vascular smooth muscle cell types and iPSCs demonstrated that the differentiation increased expression of IGFBP-3, a biomarker that has been linked to coronary heart disease [39,40], demonstrating that this model system can be used to examine mechanisms related to this marker. Additionally, annexins included in the pathways upregulated in iVSMCs have been linked to cardiovascular disease and identified as having therapeutic potential (ANXA1, ANXA2) [76,77,78,79]. This analysis demonstrated that iPSCs and iVSMCs demonstrate proteomic differences consistent with their functional states and contain biomarkers and potential therapeutic targets relevant to cardiovascular disease, supporting the utility of these cells for cardiovascular disease modeling.

3.2. Differentiation of iPSC-Derived Vascular Smooth Muscle Cells Can Be Improved by Targeting Inflammatory Pathways

Our comparison of primary cells to iVSMCs has the potential to identify target pathways for improving differentiation towards fully mature SMCs. To this end, KRT8, a protein associated with atherosclerotic lesions and indicative of synthetic SMC phenotype [52], was upregulated in iVSMCs and may indicate more proliferative vs. quiescent phenotype relative to primary cells. That being said, proteins related to phenotypic switching and vascular disease (STAT1 and PKM) [49,50] and a regulator of vascular tone (PTGIS) [51] were upregulated in primary aortic smooth muscle cells. STAT1 has been identified as a possible therapeutic target for atherosclerosis [80], and PKM and PTGIS have been identified as risk markers of atherosclerosis [81,82]. Therefore, physiologically relevant quantities of these proteins are important markers for iVSMC-based modeling of vascular disease, and these should be targeted for further maturation of iVSMCs. Interestingly, inflammatory pathways were upregulated in primary aortic smooth muscle cells, especially cytokine stimulus and neutrophil activation involved in immune response. While the exact pathways upregulated are different, the upregulation of pathways related to inflammatory signals in primary cell types versus iPSC-derived cells mirrors our previous results on work in endothelial cells [83] and suggests that systemic immune-derived signals may be important for provoking a fully mature vascular cell phenotype. In terms of cardiovascular disease relevance, cytokines are key regulators of inflammatory response, especially in abdominal aortic aneurysm pathogenesis [84], and neutrophils have been found in atherosclerotic human lesions [85] and may play a role in peripheral artery disease [86]. Thus, considering expression level of proteins in these pathways will be important for utilizing these iVSMCs for cardiovascular disease modeling.

3.3. iPSC-Derived Vascular Smooth Muscle Cells Contain Sexually Dimorphic Proteomic Differences Related to Cardiovascular Disease Risk

Finally, we compared male/female iPSCs and male/female iVSMCs to identify sexually dimorphic proteins with possible links to cardiovascular disease risk, and we examined the role of sex chromosomes in sex-based differential protein expression. Overall, the subset of sex-biased proteins in iPSCs and iVSMCs was different, demonstrating that the process of differentiation, and possibly vascular development, is impacted by different sex chromosome-related factors, resulting in uniquely affected pathways. That being said, 44 proteins in our dataset were so strongly sex linked that differentiation did not change their regulation, and in females, a higher percentage of these proteins were connected to risk of coronary artery disease or acute coronary syndrome (ARVCF, GYG2, PRCP, CTPS2, RAP2C) [87,88,89,90,91], which with further testing could relate to core intrinsic resiliency among females. GYG2 is also an x-linked protein, warranting further testing to examine its role in promoting sex-specific risk of coronary artery disease [88]. An interesting case was the protein IGFBP2, which we observed to be upregulated in females at the iPSC stage, but then upregulated in males at the iVSMC stage. Interestingly, this protein is involved in maintenance of stem cell identity in tumor and hematopoietic cell lines [92,93], and is a positive predictor of major adverse cardiovascular events [57], thus this pattern of differential sex-biased expression may indicate a benefit in female lines in maintaining stemness, but a vulnerability at the differentiated stage in male lines due to increased propensity toward de-differentiated-, proliferative-, or secretory-type SMCs. Interestingly, IGFBP2 is also a regulator of cell metabolism [94]. Out of the sexually dimorphic pathways identified here, while it is known that metabolism is sexually dimorphic, the effects of differentiation on metabolism are not well understood and warrant further exploration, especially given recent work demonstrating that lactate-based metabolic selection techniques can improve iVSMC purity without cell sorting [95]. To elucidate potential chromosomal drivers of sex bias in our data, we examined the expression of x-linked proteins relative to the expression of proteins on autosomal chromosomes. While X chromosome inactivation in females or X chromosome upregulation in males can serve to equalize protein expression, approximately 25% of X-linked genes may escape inactivation, causing sex-based expression of x-linked genes [96]. Our identification of sexually differential expression on autosomal proteins relevant to cardiovascular disease risk, coupled with previous work demonstrating sex-dependent gene regulation in atherosclerotic plaques [97], suggests that sex chromosomes may play a role in driving cardiovascular disease risk and warrants further examination.

3.4. Limitations

It is worth noting some limitations of our analysis. For one, most donors of iPSC lines were older (six of the eight were 50 years of age or older). To this end, a higher percentage of differentially expressed proteins in female iVSMCs (as compared to male iVSMCs) were related to increased risk of coronary artery disease. After menopause, due to hormonal changes, women are at higher risk of coronary artery disease [98], and aging is the top risk factor for atherosclerosis [99]. Due to the age of the majority of the female donors, many could be postmenopausal, and there is a possibility that the imprint of the effects of hormones on cellular function may not have been removed during the reprogramming process [100], causing these female iPSC cell lines to exhibit proteomic regulation consistent with a higher risk of coronary artery disease. However, prior work has shown that residual imprinting is reduced with iPSC passaging [101], and our lines have been passaged at least 25x, which may minimize this concern, though it is worth considering nonetheless. While we chose four male and four female iPSC lines to examine sex differences, the effects of other factors (such as age and race) warrant further examination. Additionally, line-to-line diversity may exist in the proteomes, and all data are provided in supplemental for further investigation into line-to-line variability. Another limitation pertains to the limits of detection of our proteomic technology: There may be key regulatory factors, particularly those linked to X chromosome genes, which were not detected in our dataset but are key to understanding sex bias. Integration of proteomics with other -omic technology (transcriptomics and Assay for Transposase-Accessible Chromatin [ATAC]-seq) could assist in fully elucidating the circuitry driving the sex-biased proteomic signatures observed in our study. Nominal (unadjusted) p values were used to threshold for statistical significance, and thus our false positive rate may be higher than the 5% cut off rate would otherwise indicate. We chose to use unadjusted p values in order to gain more power to observe DEPs and perform informative pathway analysis. Both adjusted and unadjusted p values are provided in the data supplement for the readers consideration. Finally, by design we did not consider the effect of sex hormones in this study. Past work has indicated that estrogen may enhance vascular contraction in females, suggesting that sex differences in vascular tone may related to interactions with estrogen [102], but the relationship between progesterone and vascular contraction is less clear [103,104,105]. The role of sex hormones, as opposed to sex chromosomes, in contributing to vascular pathophysiology and disease warrants further research in humanized models.

3.5. Conclusions

In summary, we have extensively characterized the proteomes of iVSMCs and shown that, although iVSMCs are highly similar to primary aortic smooth muscle cells, there are potential pathways and targets that can enhance their utility in cardiovascular disease modeling. Furthermore, iVSMCs exhibit sex differences relevant to cardiovascular disease risk, possibly due to sex-based regulation of autosomal proteins. The targets identified here can be used to enhance iVSMC differentiation to improve the biological relevance of iVSMC cardiovascular disease modeling.

4. Materials and Methods

4.1. iPSC Generation

The iPSC lines utilized in this study were generated from the peripheral blood mononuclear cells (PMBCs) or skin fibroblasts of healthy lean (BMI < 27 kg/m2) male and female controls by the iPSC Core at Cedars-Sinai Biomanufacturing Center as previously described [83], resulting in less than 5% of abnormal karyotypes of iPSCs. All undifferentiated iPSCs were maintained in mTeSR+ medium (StemCell Technologies, Vancouver, Canada) onto BD MatrigelTM matrix-coated plates. The cell lines used in this study are summarized at Supplementary Table S1. The reprogramming of iPSCs and differentiation protocols were carried out in accordance with the guidelines approved by Stem Cell Research Oversight committee (SCRO) and IRB, under the auspices of IRB-SCRO Study STUDY00000543 (Parker Lab Stem Cell Differentiation Program) and Pro00032834 (iPSC Core Repository and Stem Cell Program).

4.2. Differentiation of Vascular Smooth Muscle Cells from iPSCs (iVSMCs)

Vascular smooth muscle cells (iVSMCs) were generated from iPSCs using existing protocols [35] and phenol red-free media. Briefly, iPSCs were plated onto Matrigel-coated plates at a density of 37 K/cm2. The following day, they were induced to mesoderm using CHIR99021 (8 μM, Cayman Chemicals, Ann Arbor, MI, USA) and BMP4 (25 ng/mL, R&D Systems, Minneapolis, MN, USA) for 3 days. Vascular smooth muscle cells were generated by culturing cells in ActivinA (2 ng/mL, PeproTech, Cranbury, NJ, USA) and PDGF-BB (10 ng/mL, PeproTech) for 2 days, followed by culturing cells in ActivinA (2 ng/mL, PeproTech) and heparin (2 μg/mL, Sigma-Aldrich, St. Louis, MO, USA) from Day 6 to Day 21 with media changes every other day. Cells were replated on Day 7 and Day 11 at a density of 35.3 K/cm2 and on Day 20 at a density of 17.6K/cm2 for proteomics and Mitoplex. For imaging, cells were seeded at 46.875 K/cm2. Primary aortic smooth muscle cells were cultured in the same media used for iVSMCs on Days 6–21.

4.3. Immunofluorescence

Cells were first fixed with 4% paraformaldehyde (PFA) in phosphate-buffered saline (PBS) for 15 min and subsequently washed 3x with PBS. Fixed cells were then permeabilized and blocked in blocking buffer (10% Horse Serum [Sigma-Aldrich], 0.1% Invitrogen]) for 1 h. Samples were incubated in primary anitbodies in blocking buffer overnight at 4 °C at the following ratios: alpha-smooth muscle actin (1:250, Abcam, Cambridge, UK), Calponin (1:250, Abcam), and myosin heavy chain 11 (1:100, Abcam). After washing cells in 0.1% Tween 20 in PBS, samples were incubated in Alexa Fluor Goat Anti-Rabbit 488 (Invitrogen, Waltham, MA, USA) at a ratio of 1:2500 in blocking buffer for 1 h. Samples were washed in 0.1% Tween 20 in PBS and incubated in DAPI diluted in PBS (1:2500, Invitrogen) for 15 min and washed 3x with PBS. Immunofluorescence images were taken using appropriate fluorescent filters using an ECHO Revolve M-00151 microscope.

4.4. Contractility Assay

Cells were washed 3X in warm Physiological Saline Solutions (PSS) Buffer (118.9 mM NaCl; 4.69 mM KCl; 1.17 mM MgSO4-7H2O; 1.18 mM KH2PO4; 2.50 mM CaCl2; 25 mM NaHCO3; 0.03 mM Ethylenediaminetetraacetic acid; 5.50 mM Glucose) and incubated for 10 min on a plate warmer on an ECHO Revolve M-00151 microscope. 100 μM carbachol (Millipore Sigma, Burlington, MA, USA) was added, and an image of the initial time point was taken. After 5 min, another image of the final time point was taken. Change in cell contractility was quantified as the difference between the area of the cell at the initial time point and the area of the cell at the final time point as analyzed using ImageJ v. 2.1.0.

4.5. Proteomic Sample Preparation and Mass Spectrometry Acquisition

Cell pellets were lysed using 8 M urea/5% sodium dodecyl sulfate (Sigma-Aldrich) with 100 mM Glycine in water (Sigma-Aldrich). After determining protein concentration using a BCA assay, 50 ug of protein per sample were aliquoted. Protein aliquots were processed, digested, and cleaned using the S-TRAP system (Protifi, Fairport, NY, USA), and following elution from the S-TRAP columns, peptides were dried overnight and resuspended at 1 µg/µL for injection onto MS. DIA analysis was performed on an Orbitrap Fusion Lumos Tribrid (Thermo Scientific, Waltham, MA, USA) mass spectrometer interfaced with a microflow-nanospray electrospray ionization source (Newomics, IS-T01, Berkeley, CA, USA) coupled to UltiMate 3000™ ultra-high-pressure chromatography system with 0.1% formic acid in water as mobile phase A and 0.1% formic acid in acetonitrile as mobile phase B. Peptides were separated at an initial flow rate of 1.20 µL/minute and linearly gradient of 8–23% B for 0–70 min, 23–35% B for 70–95 min. The column was then flushed with an increased flow rate of 1.4 µL/minute and a linear gradient of 35–98% B for 95–96 min, then held at 98% B for 96–105 min before being re-equilibrated at a flow rate of 1.2 µL/min and a linearly decreasing gradient of 98%B to 8% B. The column used was Thermo Scientific™ µPac™ HPLC column with a 200cm bed length (P/N: COL-NANO200G1B). Source parameters were set to a voltage of 2100 V and a capillary temperature of 290 °C. MS1 resolution was set to 60,000 with an AGC target of 600,000 and a normalized AGC target value for fragment spectra of 150% was used. The RF Lens was set to 30% with a maximum injection time of 50 ms. Fragmented ions were detected across a scan range of 400–1000 m/z with 38 non-overlapping data independent acquisition precursor windows of size 16 Da. MS2 resolution was set to 15,000 with a scan range of 200–2000 m/z, a stepped collision energy of 5%, and for a maximum fixed collision energy of 30%, a maximum injection time of 30 ms, an AGC target of 200,0000 and a normalized AGC target of 400%. All data are acquired in profile mode using positive polarity.

4.6. Proteomic Analysis

Raw MS files were analyzed using the DIA-Neural Network platform [106], with files searched using a ‘library-free’ strategy by searching files against an in silico digested protein FASTA sequence database (Uniprot Reviewed and Canonical human sequences). Since data were acquired on balanced experimental groups at two different points in time, batch correction was performed using previously published methods [107]. The initial data visualization, processing and principal component analysis was performed using Perseus [108]. Venn diagrams were prepared using InteractiVenn (https://www.interactivenn.net/, 28 December 2023) [109]. Calculation of Log2Fold Changes for cell type (iPSC vs. iVSMC vs. primary and iPSC vs. Vascular Cell Types) and sex difference (iPSC male vs. iPSC female and iVSMC male vs. iVSMC female) comparisons were completed using MSstats 3.14. Volcano plots were prepared by plotting the log fold changes in all differentially expressed proteins in the desired comparison with a p-value less than 0.05. The protein functional networks for iPSC vs. iVSMC comparison and sex difference comparisons (iPSC male vs. iPSC female and iVSMC male vs. iVSMC female) were extracted and visualized using PINE v. 2.3.1, Cytoscape v. 3.8.2, and ClueGO v 2.5.8 [110]. Enrichr (https://maayanlab.cloud/Enrichr/, 21 December 23) [111] was used to identify gene ontologies for Primary vs. iVSMC comparison, and a clusterplot of gene ontologies was generated using R v. 4.3.3. The chromosome number of proteins was identified using Uniprot (https://www.uniprot.org/, 9 February 2023) [112] and used to generate the scatterplots.

4.7. Western Blots

Sigma Bradford Reagent protein assay kit was used for protein concentration estimation. Equal concentrations of protein for each condition were resolved on mini-protein TGX stain free gels (Bio-Rad, Hercules, CA, USA) and transferred to polyvinylidene fluoride (PVDF) membranes. Membranes were stained with Ponceau S Staining (Thermo Fisher Scientific, Waltham, MA, USA) for total protein estimation and blocked with 2.5% Bovine serum albumin (BSA) and 0.1% tween20 (TBST) in 1X Tris-buffered saline (Cell signaling) for an hour. Next, membranes were incubated with diluted primary antibodies (1:1000) (STAT1 (Cell signaling 9172T), PTGIS (Abcam ab23688), KRT8 (Thermo Fisher scientific 17514-1-AP), EGFR (Cell signaling D38B1), IGFBP2 (Cell signaling 3922S), TOMM20 (Abcam ab186735), PTGR2 (Thermo Fisher scientific PA5-101972) and ACTB (Abcam ab8227)) at 4 °C overnight. Membranes were washed with 1X TBST three times at room temperature and incubated with KPL-peroxidase labeled secondary antibodies (Seracare) for 2 h at room temperature. After membranes were washed three times with 1X TBST, immuno-reactive bands were developed with ClarityTM western ECL substrate (Bio-Rad) and imaged using ChemiDoc-Imaging system (Bio-Rad). Densitometry analysis was performed using Image J v. 1.54.

4.8. Metabolomic Sample Preparation, Mass Spectrometry Acquisition, and Analysis

Metabolites were extracted using previously published methods [64]. Metabolite extractions were analyzed with an Agilent 6470A Triple quadrupole mass spectrometer, operating in negative mode, connected to an Agilent 1290 Ultra High-Performance Liquid Chromatography (UHPLC) system, and utilizing the MassHunter Metabolomics dMRM Database and Method to scan for 219 polar metabolites within each sample. The method is a highly reproducible and robust ion-pair reversed-phase (IP-RP) chromatographic method developed to provide separation of anionic and hydrophobic metabolites. Tributylamine (TBA), a volatile ternary amine amenable to electrospray ionization that functions as an ion pair reagent was used to facilitate and improve reproducible retention of acidic metabolites. The ion-pairing LC/MS method enables simultaneous analysis of multiple metabolite functional classes, including amino acids, citric acid cycle intermediates and other carboxylic acids, nucleobases, nucleosides, phosphosugars, and fatty acids. Mobile phases consisted of HPLC or LCMS grade reagents. Buffer A is water with 3% methanol, 10 mM TBA, and 15 mM acetic acid. Buffers B and D are isopropanol and acetonitrile, respectively. Finally, Buffer C is methanol with 10 mM TBA and 15 mM acetic acid. The analytical column used was an Agilent ZORBAX RRHD Extend-C18 1.8 µm 2.1 × 150 mm coupled with a ZORBAX Extend Fast Guard column for UHPLC Extend-C18, 1.8 µm, 2.1 mm × 5mm. The MRM method takes advantage of known retention time information for each compound to create MRM transition lists that are dynamically created throughout an LC/MS run using a window around the expected retention times. In this way, compounds are only monitored while they are eluting from the LC, improving limits of detection, and permitting more metabolites to be measured within a short period of time. Resulting chromatograms were visualized in Agilent MassHunter Quantitative Analysis for QQQ. The final peaks were manually checked for consistent and proper integration. Results were analyzed in MetaboAnalyst [113].

4.9. Statistical Analyses

Statistical significance between groups was determined by a pairwise comparison. p values < 0.05 were considered statistically significant. Statistical analyses were performed using MSstats 3.14 [114], and graphs were generated using Excel v. 2402 and R 4.3.3.

Author Contributions

Conceptualization, N.R.A., S.E., R.S.S., D.S. and S.J.P.; data curation, N.R.A.; formal analysis, N.R.A., N.S., B.N., K.D., D.R., L.M. and S.J.P.; funding acquisition, N.R.A. and S.J.P.; investigation, N.R.A., D.G. and S.E.; methodology, A.S.; project administration, N.R.A., M.L.M. and S.J.P.; resources, M.L.M. and D.S.; supervision, N.R.A. and S.J.P.; visualization, N.R.A.; writing—original draft, N.R.A.; writing—review and editing, S.J.P. All authors have read and agreed to the published version of the manuscript.

 Institutional Review Board Statement

Human cell lines were obtained or created at Cedars-Sinai under the auspices of the Cedars-Sinai Medical Center Institutional Review Board (IRB) approved protocols. All subjects gave their informed consent for the generation of iPSC lines from peripheral blood mononuclear cells or skin fibroblasts out in accordance with the guidelines approved by Stem Cell Research Oversight committee (SCRO) and IRB, under the auspices of IRB-SCRO Protocols Pro00032834 (iPSC Core Repository and Stem Cell Program). The iPSC differentiation protocols in the present study were carried out in accordance with the guidelines approved by Stem Cell Research Oversight committee (SCRO) and IRB, under the auspices of Study 0000543 (Parker Lab Stem Cell Program). In vitro studies using human cell lines were conducted from participants that provided written informed consent for research studies. Remaining studies were conducted with post-mortem human specimens with appropriate IRB approvals.

 Informed Consent Statement

Not applicable.

 Data Availability Statement

The data presented in this study are openly available in MassIVE at https://doi.org/10.25345/C5B56DG77, accessed on 24 November 2024.

 Acknowledgments

We would like to acknowledge the support of the Cedars-Sinai Proteomics and Metabolomics Core facility, the Cedars-Sinai Biomanufacturing Center facility and Cedars-Sinai Research Institute in supporting this project.

 Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables
View Image - Figure 1. Generation of vascular smooth muscle cells (iVSMCs) from iPSC lines of both sexes. (A) Contractile markers (ACTA2, CNN1, and MHC11) were present in both male and female iVSMCs. (B) Contractile function, as measured by change in cell area after the addition of carbachol, for female and male iVSMCs. (C) Principal component analysis (PCA) of iVSMCs generated from 4 female and 4 male iPSC lines as compared to primary aortic smooth muscle cells. Unfilled symbols are male lines and filled symbols are female lines. (D) Venn diagram showing the number of proteins in which expression is shared by all 3 types of cells, between iVSMCs and iPSCs only, between iVSMCs and primary aortic smooth muscle cells only, or between iPSCs and primary aortic smooth muscle cells only. (E) Volcano plot of the log10 p-value of each expressed protein on vascular cell types (iVSMCs and primary aortic smooth muscle cells) vs. iPSCs. Only transcripts with a p-value of less than 0.05 were included, and those that did not demonstrate differential expression with a log2 fold-change in either direction greater than 0.5 are plotted in black. Negative values (blue) are higher in vascular smooth muscle cell types (iVSMCs and primary aortic smooth muscle cells) while positive values (orange) are higher in iPSCs.Enlarge this image.

Figure 1. Generation of vascular smooth muscle cells (iVSMCs) from iPSC lines of both sexes. (A) Contractile markers (ACTA2, CNN1, and MHC11) were present in both male and female iVSMCs. (B) Contractile function, as measured by change in cell area after the addition of carbachol, for female and male iVSMCs. (C) Principal component analysis (PCA) of iVSMCs generated from 4 female and 4 male iPSC lines as compared to primary aortic smooth muscle cells. Unfilled symbols are male lines and filled symbols are female lines. (D) Venn diagram showing the number of proteins in which expression is shared by all 3 types of cells, between iVSMCs and iPSCs only, between iVSMCs and primary aortic smooth muscle cells only, or between iPSCs and primary aortic smooth muscle cells only. (E) Volcano plot of the log10 p-value of each expressed protein on vascular cell types (iVSMCs and primary aortic smooth muscle cells) vs. iPSCs. Only transcripts with a p-value of less than 0.05 were included, and those that did not demonstrate differential expression with a log2 fold-change in either direction greater than 0.5 are plotted in black. Negative values (blue) are higher in vascular smooth muscle cell types (iVSMCs and primary aortic smooth muscle cells) while positive values (orange) are higher in iPSCs.

View Image - Figure 2. Differential expression of proteins in vascular smooth muscle cell types (iVSMCs and primary aortic smooth muscle cells) vs. iPSCs demonstrated differences in functional pathways between the cell types. (A) Proteins related to cell cycle process, nuclear DNA replication, and RNA helicase activity were upregulated in iPSCs. (B) Proteins related to focal adhesions, cadherin binding, and carbohydrate-derivative metabolic process were upregulated in iVSMCs. Red/orange correlates to proteins and pathways higher in iPSCs, and blue correlates to proteins and pathways higher in vascular smooth muscle cell types. A larger version of this image is included in Supplemental to ensure readability.Enlarge this image.

Figure 2. Differential expression of proteins in vascular smooth muscle cell types (iVSMCs and primary aortic smooth muscle cells) vs. iPSCs demonstrated differences in functional pathways between the cell types. (A) Proteins related to cell cycle process, nuclear DNA replication, and RNA helicase activity were upregulated in iPSCs. (B) Proteins related to focal adhesions, cadherin binding, and carbohydrate-derivative metabolic process were upregulated in iVSMCs. Red/orange correlates to proteins and pathways higher in iPSCs, and blue correlates to proteins and pathways higher in vascular smooth muscle cell types. A larger version of this image is included in Supplemental to ensure readability.

View Image - Figure 3. Differential expression of proteins in iVSMCs vs. primary aortic smooth muscle cells identifies potential targets for improving maturation as well as functional differences. (A) Volcano plot of the log10 p-value of each expressed protein in primary aortic smooth muscle cells vs. iVSMCs. Negative values (blue) are higher in primary aortic smooth muscle cells while positive values (orange) are higher in iVSMCs. Only transcripts with a p-value of less than 0.05 were included, and those that did not demonstrate differential expression with a log2 fold-change in either direction greater than 0.5 are plotted in black. (B) Comparative pathway analysis revealed differences in inflammatory pathways and pathways related to cell–cell and adherens junctions. Western blots (C) and the densitometry analysis (D) for key proteins KRT8, STAT-1, and PTGIS confirm trends observed in proteomic data analysis. Data were normalized using β-actin (ACTB).Enlarge this image.

Figure 3. Differential expression of proteins in iVSMCs vs. primary aortic smooth muscle cells identifies potential targets for improving maturation as well as functional differences. (A) Volcano plot of the log10 p-value of each expressed protein in primary aortic smooth muscle cells vs. iVSMCs. Negative values (blue) are higher in primary aortic smooth muscle cells while positive values (orange) are higher in iVSMCs. Only transcripts with a p-value of less than 0.05 were included, and those that did not demonstrate differential expression with a log2 fold-change in either direction greater than 0.5 are plotted in black. (B) Comparative pathway analysis revealed differences in inflammatory pathways and pathways related to cell–cell and adherens junctions. Western blots (C) and the densitometry analysis (D) for key proteins KRT8, STAT-1, and PTGIS confirm trends observed in proteomic data analysis. Data were normalized using β-actin (ACTB).

View Image - Figure 4. Sex-based differential expression of proteins in iPSCs and iVSMCs identifies potential targets that contribute to sex-based differences in cardiovascular disease risk. Volcano plot of the log10 p-value of each expressed protein on female vs. male iPSCs (A) and iVSMCs (B). Negative values (blue) are higher in females while positive values (orange) are higher in males. Transcripts with a p-value of less than 0.05 were included and differential expressions with a log2 fold-change in either direction greater than 0.5 are plotted in black. Transcripts upregulated in females with an adjusted p-value greater than 0.05 are green, while transcripts upregulated in males with an adjusted p-value greater than 0.05 are red. (C) Venn diagram demonstrating the commonality in proteins that are differentially expressed by sex in iPSCs and iVSMCs. Western blots (D,E) and densitometry analysis (F,G) confirm trends in key proteins in iPSCs (PTGR2 and TOMM20) as well as in key proteins in iVSMCs (EGFR and IGFBP2). Data were normalized using total protein because ACTB levels were differential by sex in iPSCs.Enlarge this image.

Figure 4. Sex-based differential expression of proteins in iPSCs and iVSMCs identifies potential targets that contribute to sex-based differences in cardiovascular disease risk. Volcano plot of the log10 p-value of each expressed protein on female vs. male iPSCs (A) and iVSMCs (B). Negative values (blue) are higher in females while positive values (orange) are higher in males. Transcripts with a p-value of less than 0.05 were included and differential expressions with a log2 fold-change in either direction greater than 0.5 are plotted in black. Transcripts upregulated in females with an adjusted p-value greater than 0.05 are green, while transcripts upregulated in males with an adjusted p-value greater than 0.05 are red. (C) Venn diagram demonstrating the commonality in proteins that are differentially expressed by sex in iPSCs and iVSMCs. Western blots (D,E) and densitometry analysis (F,G) confirm trends in key proteins in iPSCs (PTGR2 and TOMM20) as well as in key proteins in iVSMCs (EGFR and IGFBP2). Data were normalized using total protein because ACTB levels were differential by sex in iPSCs.

View Image - Figure 5. Differential expression of proteins by sex in iPSCs and iVSMCs demonstrated differences in functional pathways between the cell types. In female iPSCs, proteins related to mitochondrion and ATP binding were upregulated (A). Proteins related to ATP binding, glycogen metabolic process, and cadherin binding were upregulated in male iVSMCs (B). Red/orange correlates to proteins and pathways that are higher in males, and blue correlates to proteins and pathways that are higher in females. A larger version of this image is included in the supplemental material to ensure readability.Enlarge this image.

Figure 5. Differential expression of proteins by sex in iPSCs and iVSMCs demonstrated differences in functional pathways between the cell types. In female iPSCs, proteins related to mitochondrion and ATP binding were upregulated (A). Proteins related to ATP binding, glycogen metabolic process, and cadherin binding were upregulated in male iVSMCs (B). Red/orange correlates to proteins and pathways that are higher in males, and blue correlates to proteins and pathways that are higher in females. A larger version of this image is included in the supplemental material to ensure readability.

View Image - Figure 6. Differentiation causes sex-based effects in metabolite production. Sex-specific metabolite expression varies in iPSCs (A) and iVSMCs (B).Enlarge this image.

Figure 6. Differentiation causes sex-based effects in metabolite production. Sex-specific metabolite expression varies in iPSCs (A) and iVSMCs (B).

View Image - Figure 7. Sex-specific regulation of proteins occurs on autosomal and sex chromosomes. (A) Regulation of differentially expressed x-linked proteins in iPSCs and iVSMCs. Examination of the chromosome distribution of differentially expressed proteins by sex in iPSCs (B) and iVSMCs (C), including in proteins relevant to cardiovascular disease: TOMM20 (D) and PTGR2 (E) in iPSCs and IGFBP2 (F) and SULTE1 (G) in iVSMCs.Enlarge this image.

Figure 7. Sex-specific regulation of proteins occurs on autosomal and sex chromosomes. (A) Regulation of differentially expressed x-linked proteins in iPSCs and iVSMCs. Examination of the chromosome distribution of differentially expressed proteins by sex in iPSCs (B) and iVSMCs (C), including in proteins relevant to cardiovascular disease: TOMM20 (D) and PTGR2 (E) in iPSCs and IGFBP2 (F) and SULTE1 (G) in iVSMCs.

Proteins associated with increased cardiovascular disease risk or increased protection against disease in iVSMCs.

Disease Proteins Log2 Fold Change p-Value PMID
Upregulated in Females, Increased Risk
(Generalized) Vascular Disease: 44 VWA5A −1.46039 0.002311 38292008
DCLK1 −1.52752 8.28 × 10−8 36896602
ECI2 −0.31627 0.021736 37827202
FGG −1.14722 0.003563 33304927
ARG1 −0.75068 0.02759 30526064 & 36252109
BCHE −0.74068 0.045869 12387587
VCL −0.28687 0.016281 38287421
HK1 −0.27593 0.016379 26351984
SLC9A1 −1.02283 0.005161 34440087
IGFBP5 −0.56078 0.020567 32194505
CANX −0.16783 0.044018 22582288
CCN2 −0.86413 0.008887 36625347
LMOD1 −0.51757 0.040023 30444878
TSPO −0.90308 0.047452 31515530
STAT3 −0.26407 0.000307 22418922
TNNT2 −0.40919 0.013283 37555319
CEBPD −0.68694 0.018908 25614278
SULT1E1 −1.76051 0.000419 23685729
ALDH3A2 −0.3739 0.000969 36224302
MAP2K6 −0.62113 0.023733 23175675
AGFG1 −0.35592 0.042249 35178459
ATP1B3 −0.35459 0.018878 36172868
RAB15 −0.81633 0.006207 36231021 & 26305474
SNRPD2 −0.34716 0.038198 32981416
RELN −0.58436 0.031262 26980442
TFAM −0.79791 0.030241 36834896 & 36312244
TFCP2 −0.7615 0.001191 36889372
LSAMP −0.47517 0.016794 18318786
KLHL22 −0.56247 0.04838 35959094
DDX59 −0.95341 0.005199 30444878 & 32591598
NECAP1 −0.46111 0.01405 34565095
LPCAT1 −0.33223 0.003957 32396387
SMARCD1 −0.40763 0.021485 32787523
NEDD4L −0.53192 0.017933 28029182
CNN2 −0.47333 0.005439 26970176 & 27575021
DPYSL5 −1.03699 0.025921 23118132 & 17554300
CHMP4B −0.40327 0.023958 37827202
ANTXR1 −0.49032 0.036162 26785120
KAT8 −0.92683 0.016858 30355081 & 38334359
DYNLRB1 −0.99106 0.031444 28530674
DIP2B −0.69272 0.005777 29491472
AGO2 −0.26446 0.018211 32151690 & 36924230
TPRKB −1.10421 0.01224 28902926
TCAF1 −0.44215 0.04401 34121455
Coronary Artery Disease: 18 ARG1 −0.75068 0.02759 30526064 & 36252109
BCHE −0.74068 0.045869 12387587
HK1 −0.27593 0.016379 26351984
IGFBP5 −0.56078 0.020567 32194505
LMOD1 −0.51757 0.040023 30444878
ATP5MF −0.29421 0.033782 6433946
PSMC1 −0.15173 0.037424 26986213
SNRPD2 −0.34716 0.038198 32981416
TFCP2 −0.7615 0.001191 36889372
LSAMP −0.47517 0.016794 18318786
KLHL22 −0.56247 0.04838 35959094
DDX59 −0.95341 0.005199 30444878 & 32591598
NECAP1 −0.46111 0.01405 34565095
LPCAT1 −0.33223 0.003957 32396387
KAT8 −0.92683 0.016858 30355081 & 38334359
DYNLRB1 −0.99106 0.031444 28530674
DIP2B −0.69272 0.005777 29491472
TCAF1 −0.44215 0.04401 34121455
Atherosclerosis: 14 VWA5A −1.46039 0.002311 38292008
DCLK1 −1.52752 8.28 × 10−8 36896602
VCL −0.28687 0.016281 38287421
CANX −0.16783 0.044018 22582288
TSPO −0.90308 0.047452 31515530
CEBPD −0.68694 0.018908 25614278
SULT1E1 −1.76051 0.000419 23685729
ALDH3A2 −0.3739 0.000969 36224302
MAP2K6 −0.62113 0.023733 23175675
RAB15 −0.81633 0.006207 36231021; 26305474
RELN −0.58436 0.031262 26980442
NEDD4L −0.53192 0.017933 28029182
CNN2 −0.47333 0.005439 26970176; 27575021
TPRKB −1.10421 0.01224 28902926
Aneurysm or Aortic Dissection: 8 ECI2 −0.31627 0.021736 37827202
CCN2 −0.86413 0.008887 36625347
TNNT2 −0.40919 0.013283 37555319
AGFG1 −0.35592 0.042249 35178459
ATP1B3 −0.35459 0.018878 36172868
SMARCD1 −0.40763 0.021485 32787523
CHMP4B −0.40327 0.023958 37827202
AGO2 −0.26446 0.018211 32151690
Upregulated in Males, Increased Risk
Vascular Disease: 24 KRIT1 1.043785 0.028861 31590384
SLC31A1 0.472014 0.002904 37442861
STRN 0.360777 0.007765 31821324
ATP1B1 0.794664 0.002144 32591598
ALPL 1.284741 0.00392 37080965
TPM3 0.809477 0.048813 33341060
UCHL1 0.518028 0.043144 34130526
JUP 0.613185 0.001758 23110151
IGFBP2 1.624962 0.015859 24548188
DPP4 0.471901 0.044796 34489872
SLC7A1 0.409248 0.006779 17325243
ADK 0.363695 0.028368 32352607
RPS3A 0.433537 0.021526 30131868
RPL7A 0.315849 0.009246 34355024
TRAF2 0.31448 0.04888 35252400
CASP8 0.500197 0.03471 28633917
PDCD4 0.650662 0.028211 20357187
CCDC80 0.632236 0.013123 30439364
RIT1 0.696099 0.009327 27101134
TNPO1 0.342378 0.011812 32714363
MYDGF 0.737334 0.021155 34020949
MORF4L1 0.486932 0.032763 36139449
FBXO3 0.530685 0.027893 30448480
TDRKH 0.94692 0.028946 35735005
Coronary Artery Disease: 7 STRN 0.360777 0.007765 31821324
ATP1B1 0.794664 0.002144 32591598
JUP 0.613185 0.001758 23110151
RPS3A 0.433537 0.021526 30131868
CASP8 0.500197 0.03471 28633917
MORF4L1 0.486932 0.032763 36139449
TDRKH 0.94692 0.028946 35735005
Atherosclerosis: 10 SLC31A1 0.472014 0.002904 37442861
JUP 0.613185 0.001758 23110151
ADK 0.363695 0.028368 32352607
RPS3A 0.433537 0.021526 30131868
TRAF2 0.31448 0.04888 35252400
PDCD4 0.650662 0.028211 20357187
CCDC80 0.632236 0.013123 30439364
TNPO1 0.342378 0.011812 32714363
MYDGF 0.737334 0.021155 34020949
FBXO3 0.530685 0.027893 30448480
Aneurysm or Aortic Dissection: 1 RPL7A 0.315849 0.009246 34355024
Upregulated in Females, Protective
Vascular Disease: 15 PLPP3 −0.42484 0.040503 35383201
ICMT −0.63458 0.008933 33526168
TGM2 −0.69476 0.032485 34326316
PRDX2 −0.37369 0.006572 33791345
MTHFS −0.61676 0.043092 22649255 & 37986948 & 28413470
CTSC −1.2166 0.001267 31926332
RAB5B −0.29669 0.031891 30333257
VBP1 −0.27658 0.049282 23685742
SNRPD1 −0.51296 0.032681 37827202
IAH1 −0.98764 0.038594 35280433
TBC1D9 −0.73983 0.027778 34712662
ATG9A −0.53849 0.004889 35090378
PHACTR4 −0.63342 0.043503 37225873 & 25143458
PXDN −0.43302 2.49 × 10−5 35219848
GGH −0.52561 0.022627 22649255
Coronary Artery Disease: none
Atherosclerosis: 7 PRDX2 −0.37369 0.006572 33791345
CTSC −1.2166 0.001267 31926332
RAB5B −0.29669 0.031891 30333257
TBC1D9 −0.73983 0.027778 34712662
ATG9A −0.53849 0.004889 35090378
PHACTR4 −0.63342 0.043503 37225873 & 25143458
PXDN −0.43302 2.49 × 10−5 35219848
Aneurysm or Aortic Dissection: 3 PLPP3 −0.42484 0.040503 35383201
SNRPD1 −0.51296 0.032681 37827202
IAH1 −0.98764 0.038594 35280433
Upregulated in Males, Protective
Vascular Disease: 9 NUDT1 0.477825 0.036192 33021405
PLA2G4A 0.775001 0.009938 31092728
GCLC 0.582604 0.002232 18035085
LRP1 0.247456 0.01096 24504736
ARHGEF7 0.275253 0.036114 35047576
ITPR2 0.49344 0.000511 27777977
PCK2 1.149266 0.001643 35982907
PCOLCE2 0.729929 0.014758 34551590
PROCR 0.911296 0.010742 35264566
Coronary Artery Disease: 2 PLA2G4A 0.775001 0.009938 31092728
PROCR 0.911296 0.010742 35264566
Atherosclerosis: 5 GCLC 0.582604 0.002232 18035085
LRP1 0.247456 0.01096 24504736
ARHGEF7 0.275253 0.036114 35047576
PCK2 1.149266 0.001643 35982907
PCOLCE2 0.729929 0.014758 34551590
Aneurysm or Aortic Dissection: 0

Note: References for each protein are listed in Supplementary Tables S6 and S7.

X-linked proteins associated with increased cardiovascular disease risk in iVSMCs of both sexes.

Disease Proteins Log2 Fold Change p-Value Pubmed
Upregulated in males:
Turner Syndrome/Aortic Dissection: 1 ASMTL 0.810314 0.004344 22707402
Upregulated in females:
Cardiovascular Disease: 10 GYG2 −0.4331 0.02961 32541024
PLS3 −0.61357 0.000687 31694393
VBP1 −0.27658 0.049282 27374120
RPS4X −0.50842 0.000903 37889192
PUDP −0.89762 0.000191 37889192
RBBP7 −0.55877 0.002332 37154037
SYAP1 −1.01159 6.08 × 10−5 37813462 & 34121455
MXRA5 −0.6801 0.03062 38292008
CTPS2 −0.52107 0.01793 38454353
RAP2C −0.55334 0.001535 33921168
Coronary Artery Disease: 2 GYG2 −0.4331 0.02961 32541024
SYAP1 −1.01159 6.08 × 10−5 37813462 & 34121455
Atherosclerosis: 7 GYG2 −0.4331 0.02961 32541024
VBP1 −0.27658 0.049282 27374120
RPS4X −0.50842 0.000903 37889192
PUDP −0.89762 0.000191 37889192
MXRA5 −0.6801 0.03062 38292008
CTPS2 −0.52107 0.01793 38454353
RAP2C −0.55334 0.001535 33921168
Aneurysm/Aortic Dissection: 2 PLS3 −0.61357 0.000687 31694393
SYAP1 −1.01159 6.08 × 10−5 37813462 & 34121455

Note: References for each protein are listed in Supplementary Table S10.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms26010187/s1.

References

1. Sayed, A.; Abramov, D.; Fonarow, G.C.; Mamas, M.A.; Kobo, O.; Butler, J.; Fudim, M. Reversals in the Decline of Heart Failure Mortality in the US, 1999 to 2021. JAMA Cardiol.; 2024; 9, pp. 585-589. [DOI: https://dx.doi.org/10.1001/jamacardio.2024.0615]

2. Perk, J.; De Backer, G.; Gohlke, H.; Graham, I.; Reiner, Z.; Verschuren, W.M.; Albus, C.; Benlian, P.; Boysen, G.; Cifkova, R. et al. European guidelines on cardiovascular disease prevention in clinical practice (version 2012): The fifth joint task force of the European society of cardiology and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of nine societies and by invited experts). Int. J. Behav. Med.; 2012; 19, pp. 403-488. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23093473]

3. Huang, Y.; Hui, Q.; Gwinn, M.; Hu, Y.J.; Quyyumi, A.A.; Vaccarino, V.; Sun, Y.V. Sexual Differences in Genetic Predisposition of Coronary Artery Disease. Circ. Genom. Precis. Med.; 2021; 14, e003147. [DOI: https://dx.doi.org/10.1161/CIRCGEN.120.003147]

4. Appelros, P.; Stegmayr, B.; Terent, A. Sex differences in stroke epidemiology: A systematic review. Stroke; 2009; 40, pp. 1082-1090. [DOI: https://dx.doi.org/10.1161/STROKEAHA.108.540781] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19211488]

5. Writing Group, M.; Mozaffarian, D.; Benjamin, E.J.; Go, A.S.; Arnett, D.K.; Blaha, M.J.; Cushman, M.; Das, S.R.; de Ferranti, S.; Despres, J.P. et al. Heart Disease and Stroke Statistics-2016 Update: A Report From the American Heart Association. Circulation; 2016; 133, pp. e38-e360.

6. Katz, D.J.; Stanley, J.C.; Zelenock, G.B. Gender differences in abdominal aortic aneurysm prevalence, treatment, and outcome. J. Vasc. Surg.; 1997; 25, pp. 561-568. [DOI: https://dx.doi.org/10.1016/S0741-5214(97)70268-4] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/9081139]

7. Singh, K.; Bonaa, K.H.; Jacobsen, B.K.; Bjork, L.; Solberg, S. Prevalence of and risk factors for abdominal aortic aneurysms in a population-based study: The Tromso Study. Am. J. Epidemiol.; 2001; 154, pp. 236-244. [DOI: https://dx.doi.org/10.1093/aje/154.3.236] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/11479188]

8. Skibba, A.A.; Evans, J.R.; Hopkins, S.P.; Yoon, H.R.; Katras, T.; Kalbfleisch, J.H.; Rush, D.S. Reconsidering gender relative to risk of rupture in the contemporary management of abdominal aortic aneurysms. J. Vasc. Surg.; 2015; 62, pp. 1429-1436. [DOI: https://dx.doi.org/10.1016/j.jvs.2015.07.079]

9. Wentzel, J.J.; Papafaklis, M.I.; Antoniadis, A.P.; Takahashi, S.; Cefalo, N.V.; Cormier, M.; Saito, S.; Coskun, A.U.; Stone, P.H. Sex-related differences in plaque characteristics and endothelial shear stress related plaque-progression in human coronary arteries. Atherosclerosis; 2022; 342, pp. 9-18. [DOI: https://dx.doi.org/10.1016/j.atherosclerosis.2021.12.013] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34999306]

10. Maas, A.H.; Appelman, Y.E. Gender differences in coronary heart disease. Neth. Heart J.; 2010; 18, pp. 598-602. [DOI: https://dx.doi.org/10.1007/s12471-010-0841-y] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21301622]

11. Locorotondo, L.G.a.G. Gender differences in cardiovascular disease. J. Integr. Cardiol.; 2015; 1, pp. 20-22.

12. Zujie Gao, Z.C. Anqiang Sun, Xiaoyan Deng, Gender differences in cardiovascular disease. Med. Nov. Technol. Devices; 2019; 4, 100025.

13. Ndzie Noah, M.L.; Adzika, G.K.; Mprah, R.; Adekunle, A.O.; Adu-Amankwaah, J.; Sun, H. Sex-Gender Disparities in Cardiovascular Diseases: The Effects of Estrogen on eNOS, Lipid Profile, and NFATs During Catecholamine Stress. Front. Cardiovasc. Med.; 2021; 8, 639946. [DOI: https://dx.doi.org/10.3389/fcvm.2021.639946]

14. Miike, K.; Aoki, M.; Yamashita, R.; Takegawa, Y.; Saya, H.; Miike, T.; Yamamura, K. Proteome profiling reveals gender differences in the composition of human serum. Proteomics; 2010; 10, pp. 2678-2691. [DOI: https://dx.doi.org/10.1002/pmic.200900496]

15. Kim, C.X.; Bailey, K.R.; Klee, G.G.; Ellington, A.A.; Liu, G.; Mosley, T.H., Jr.; Rehman, H.; Kullo, I.J. Sex and ethnic differences in 47 candidate proteomic markers of cardiovascular disease: The Mayo Clinic proteomic markers of arteriosclerosis study. PLoS ONE; 2010; 5, e9065. [DOI: https://dx.doi.org/10.1371/journal.pone.0009065] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20140090]

16. Yan, L.; Ge, H.; Li, H.; Lieber, S.C.; Natividad, F.; Resuello, R.R.; Kim, S.J.; Akeju, S.; Sun, A.; Loo, K. et al. Gender-specific proteomic alterations in glycolytic and mitochondrial pathways in aging monkey hearts. J. Mol. Cell Cardiol.; 2004; 37, pp. 921-929. [DOI: https://dx.doi.org/10.1016/j.yjmcc.2004.06.012]

17. Babiker, F.A.; Lips, D.; Meyer, R.; Delvaux, E.; Zandberg, P.; Janssen, B.; van Eys, G.; Grohe, C.; Doevendans, P.A. Estrogen receptor beta protects the murine heart against left ventricular hypertrophy. Arterioscler. Thromb. Vasc. Biol.; 2006; 26, pp. 1524-1530. [DOI: https://dx.doi.org/10.1161/01.ATV.0000223344.11128.23] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16627800]

18. Liang, W.; Ward, L.J.; Karlsson, H.; Ljunggren, S.A.; Li, W.; Lindahl, M.; Yuan, X.M. Distinctive proteomic profiles among different regions of human carotid plaques in men and women. Sci. Rep.; 2016; 6, 26231. [DOI: https://dx.doi.org/10.1038/srep26231] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27198765]

19. Theofilatos, K.; Stojkovic, S.; Hasman, M.; van der Laan, S.W.; Baig, F.; Barallobre-Barreiro, J.; Schmidt, L.E.; Yin, S.; Yin, X.; Burnap, S. et al. Proteomic Atlas of Atherosclerosis: The Contribution of Proteoglycans to Sex Differences, Plaque Phenotypes, and Outcomes. Circ. Res.; 2023; 133, pp. 542-558. [DOI: https://dx.doi.org/10.1161/CIRCRESAHA.123.322590] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37646165]

20. Agrawal, A.; Lourenco, E.V.; Gupta, S.; La Cava, A. Gender-Based Differences in Leptinemia in Healthy Aging, Non-obese Individuals Associate with Increased Marker of Oxidative Stress. Int. J. Clin. Exp. Med.; 2008; 1, pp. 305-309.

21. Vassalle, C.; Mercuri, A.; Maffei, S. Oxidative status and cardiovascular risk in women: Keeping pink at heart. World J. Cardiol.; 2009; 1, pp. 26-30. [DOI: https://dx.doi.org/10.4330/wjc.v1.i1.26] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21160573]

22. Vassalle, C.; Maffei, S.; Boni, C.; Zucchelli, G.C. Gender-related differences in oxidative stress levels among elderly patients with coronary artery disease. Fertil. Steril.; 2008; 89, pp. 608-613. [DOI: https://dx.doi.org/10.1016/j.fertnstert.2007.03.052] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/17548077]

23. Kander, M.C.; Cui, Y.; Liu, Z. Gender difference in oxidative stress: A new look at the mechanisms for cardiovascular diseases. J. Cell. Mol. Med.; 2017; 21, pp. 1024-1032. [DOI: https://dx.doi.org/10.1111/jcmm.13038] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27957792]

24. Frijhoff, J.; Winyard, P.G.; Zarkovic, N.; Davies, S.S.; Stocker, R.; Cheng, D.; Knight, A.R.; Taylor, E.L.; Oettrich, J.; Ruskovska, T. et al. Clinical Relevance of Biomarkers of Oxidative Stress. Antioxid. Redox Signal; 2015; 23, pp. 1144-1170. [DOI: https://dx.doi.org/10.1089/ars.2015.6317]

25. Glass, C.K.; Witztum, J.L. Atherosclerosis. the road ahead. Cell; 2001; 104, pp. 503-516. [DOI: https://dx.doi.org/10.1016/S0092-8674(01)00238-0]

26. Harrison, D.; Griendling, K.K.; Landmesser, U.; Hornig, B.; Drexler, H. Role of oxidative stress in atherosclerosis. Am. J. Cardiol.; 2003; 91, pp. 7A-11A. [DOI: https://dx.doi.org/10.1016/S0002-9149(02)03144-2]

27. Schnabel, R.; Blankenberg, S. Oxidative stress in cardiovascular disease: Successful translation from bench to bedside?. Circulation; 2007; 116, pp. 1338-1340. [DOI: https://dx.doi.org/10.1161/CIRCULATIONAHA.107.728394]

28. Santilli, F.; D’Ardes, D.; Davi, G. Oxidative stress in chronic vascular disease: From prediction to prevention. Vascul Pharmacol.; 2015; 74, pp. 23-37. [DOI: https://dx.doi.org/10.1016/j.vph.2015.09.003] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26363473]

29. Tabassum, R.; Widen, E.; Ripatti, S. Effect of biological sex on human circulating lipidome: An overview of the literature. Atherosclerosis; 2023; 384, 117274. [DOI: https://dx.doi.org/10.1016/j.atherosclerosis.2023.117274]

30. Atkins, S.K.; Sonawane, A.R.; Brouwhuis, R.; Barrientos, J.; Ha, A.; Rogers, M.; Tanaka, T.; Okui, T.; Kuraoka, S.; Singh, S.A. et al. Induced pluripotent stem cell-derived smooth muscle cells to study cardiovascular calcification. Front. Cardiovasc. Med.; 2022; 9, 925777. [DOI: https://dx.doi.org/10.3389/fcvm.2022.925777] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35958427]

31. Li, Y.; Wen, Y.; Green, M.; Cabral, E.K.; Wani, P.; Zhang, F.; Wei, Y.; Baer, T.M.; Chen, B. Cell sex affects extracellular matrix protein expression and proliferation of smooth muscle progenitor cells derived from human pluripotent stem cells. Stem Cell Res. Ther.; 2017; 8, 156. [DOI: https://dx.doi.org/10.1186/s13287-017-0606-2]

32. Liu, L.; Jouve, C.; Henry, J.; Berrandou, T.E.; Hulot, J.S.; Georges, A.; Bouatia-Naji, N. Genomic, Transcriptomic, and Proteomic Depiction of Induced Pluripotent Stem Cells-Derived Smooth Muscle Cells As Emerging Cellular Models for Arterial Diseases. Hypertension; 2023; 80, pp. 740-753. [DOI: https://dx.doi.org/10.1161/HYPERTENSIONAHA.122.19733]

33. Nakamura, K.; Dalal, A.R.; Yokoyama, N.; Pedroza, A.J.; Kusadokoro, S.; Mitchel, O.; Gilles, C.; Masoudian, B.; Leipzig, M.; Casey, K.M. et al. Lineage-Specific Induced Pluripotent Stem Cell-Derived Smooth Muscle Cell Modeling Predicts Integrin Alpha-V Antagonism Reduces Aortic Root Aneurysm Formation in Marfan Syndrome Mice. Arterioscler. Thromb. Vasc. Biol.; 2023; 43, pp. 1134-1153. [DOI: https://dx.doi.org/10.1161/ATVBAHA.122.318448]

34. Iosef, C.; Pedroza, A.J.; Cui, J.Z.; Dalal, A.R.; Arakawa, M.; Tashima, Y.; Koyano, T.K.; Burdon, G.; Churovich, S.M.P.; Orrick, J.O. et al. Quantitative proteomics reveal lineage-specific protein profiles in iPSC-derived Marfan syndrome smooth muscle cells. Sci. Rep.; 2020; 10, 20392. [DOI: https://dx.doi.org/10.1038/s41598-020-77274-w]

35. Patsch, C.; Challet-Meylan, L.; Thoma, E.C.; Urich, E.; Heckel, T.; O’Sullivan, J.F.; Grainger, S.J.; Kapp, F.G.; Sun, L.; Christensen, K. et al. Generation of vascular endothelial and smooth muscle cells from human pluripotent stem cells. Nat. Cell Biol.; 2015; 17, pp. 994-1003. [DOI: https://dx.doi.org/10.1038/ncb3205]

36. Raines, E.W. The extracellular matrix can regulate vascular cell migration, proliferation, and survival: Relationships to vascular disease. Int. J. Exp. Pathol.; 2000; 81, pp. 173-182. [DOI: https://dx.doi.org/10.1046/j.1365-2613.2000.00155.x]

37. Licht, A.H.; Nubel, T.; Feldner, A.; Jurisch-Yaksi, N.; Marcello, M.; Demicheva, E.; Hu, J.H.; Hartenstein, B.; Augustin, H.G.; Hecker, M. et al. Junb regulates arterial contraction capacity, cellular contractility, and motility via its target Myl9 in mice. J. Clin. Investig.; 2010; 120, pp. 2307-2318. [DOI: https://dx.doi.org/10.1172/JCI41749]

38. Hsieh, T.; Gordon, R.E.; Clemmons, D.R.; Busby, W.H., Jr.; Duan, C. Regulation of vascular smooth muscle cell responses to insulin-like growth factor (IGF)-I by local IGF-binding proteins. J. Biol. Chem.; 2003; 278, pp. 42886-42892. [DOI: https://dx.doi.org/10.1074/jbc.M303835200]

39. Kaplan, R.C.; McGinn, A.P.; Pollak, M.N.; Kuller, L.H.; Strickler, H.D.; Rohan, T.E.; Cappola, A.R.; Xue, X.; Psaty, B.M. Association of total insulin-like growth factor-I, insulin-like growth factor binding protein-1 (IGFBP-1), and IGFBP-3 levels with incident coronary events and ischemic stroke. J. Clin. Endocrinol. Metab.; 2007; 92, pp. 1319-1325. [DOI: https://dx.doi.org/10.1210/jc.2006-1631]

40. Giovannini, S.; Cesari, M.; Marzetti, E.; Leeuwenburgh, C.; Maggio, M.; Pahor, M. Effects of ACE-inhibition on IGF-1 and IGFBP-3 concentrations in older adults with high cardiovascular risk profile. J. Nutr. Health Aging; 2010; 14, pp. 457-460. [DOI: https://dx.doi.org/10.1007/s12603-010-0036-7]

41. Xu, M.; Millard, R.W.; Ashraf, M. Role of GATA-4 in differentiation and survival of bone marrow mesenchymal stem cells. Prog. Mol. Biol. Transl. Sci.; 2012; 111, pp. 217-241. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22917233]

42. Lim, Y.S.; Tang, B.L. The Evi5 family in cellular physiology and pathology. FEBS Lett.; 2013; 587, pp. 1703-1710. [DOI: https://dx.doi.org/10.1016/j.febslet.2013.04.036]

43. Coronado, D.; Godet, M.; Bourillot, P.Y.; Tapponnier, Y.; Bernat, A.; Petit, M.; Afanassieff, M.; Markossian, S.; Malashicheva, A.; Iacone, R. et al. A short G1 phase is an intrinsic determinant of naive embryonic stem cell pluripotency. Stem Cell Res.; 2013; 10, pp. 118-131. [DOI: https://dx.doi.org/10.1016/j.scr.2012.10.004] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23178806]

44. Gonzales, K.A.; Liang, H.; Lim, Y.S.; Chan, Y.S.; Yeo, J.C.; Tan, C.P.; Gao, B.; Le, B.; Tan, Z.Y.; Low, K.Y. et al. Deterministic Restriction on Pluripotent State Dissolution by Cell-Cycle Pathways. Cell; 2015; 162, pp. 564-579. [DOI: https://dx.doi.org/10.1016/j.cell.2015.07.001]

45. Neganova, I.; Tilgner, K.; Buskin, A.; Paraskevopoulou, I.; Atkinson, S.P.; Peberdy, D.; Passos, J.F.; Lako, M. CDK1 plays an important role in the maintenance of pluripotency and genomic stability in human pluripotent stem cells. Cell Death Dis.; 2014; 5, e1508. [DOI: https://dx.doi.org/10.1038/cddis.2014.464]

46. Paniza, T.; Deshpande, M.; Wang, N.; O’Neil, R.; Zuccaro, M.V.; Smith, M.E.; Madireddy, A.; James, D.; Ecker, J.; Rosenwaks, Z. et al. Pluripotent stem cells with low differentiation potential contain incompletely reprogrammed DNA replication. J. Cell Biol.; 2020; 219, e201909163. [DOI: https://dx.doi.org/10.1083/jcb.201909163]

47. Di Stefano, B.; Luo, E.C.; Haggerty, C.; Aigner, S.; Charlton, J.; Brumbaugh, J.; Ji, F.; Rabano Jimenez, I.; Clowers, K.J.; Huebner, A.J. et al. The RNA Helicase DDX6 Controls Cellular Plasticity by Modulating P-Body Homeostasis. Cell Stem Cell; 2019; 25, pp. 622-638.e13. [DOI: https://dx.doi.org/10.1016/j.stem.2019.08.018] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31588046]

48. Li, D.; Yang, J.; Malik, V.; Huang, Y.; Huang, X.; Zhou, H.; Wang, J. An RNAi screen of RNA helicases identifies eIF4A3 as a regulator of embryonic stem cell identity. Nucleic Acids Res.; 2022; 50, pp. 12462-12479. [DOI: https://dx.doi.org/10.1093/nar/gkac1084]

49. Kirchmer, M.N.; Franco, A.; Albasanz-Puig, A.; Murray, J.; Yagi, M.; Gao, L.; Dong, Z.M.; Wijelath, E.S. Modulation of vascular smooth muscle cell phenotype by STAT-1 and STAT-3. Atherosclerosis; 2014; 234, pp. 169-175. [DOI: https://dx.doi.org/10.1016/j.atherosclerosis.2014.02.029]

50. Jain, M.; Dhanesha, N.; Doddapattar, P.; Nayak, M.K.; Guo, L.; Cornelissen, A.; Lentz, S.R.; Finn, A.V.; Chauhan, A.K. Smooth Muscle Cell-Specific PKM2 (Pyruvate Kinase Muscle 2) Promotes Smooth Muscle Cell Phenotypic Switching and Neointimal Hyperplasia. Arterioscler. Thromb. Vasc. Biol.; 2021; 41, pp. 1724-1737. [DOI: https://dx.doi.org/10.1161/ATVBAHA.121.316021] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33691477]

51. Manalo, D.J.; Rowan, A.; Lavoie, T.; Natarajan, L.; Kelly, B.D.; Ye, S.Q.; Garcia, J.G.; Semenza, G.L. Transcriptional regulation of vascular endothelial cell responses to hypoxia by HIF-1. Blood; 2005; 105, pp. 659-669. [DOI: https://dx.doi.org/10.1182/blood-2004-07-2958]

52. Moon, M.C.; Yau, L.; Wright, B.; Zahradka, P. Injury-induced expression of cytokeratins 8 and 18 by vascular smooth muscle cells requires concurrent activation of cytoskeletal and growth factor receptors. Can. J. Physiol. Pharmacol.; 2008; 86, pp. 223-231. [DOI: https://dx.doi.org/10.1139/Y08-019]

53. Kaiser-Graf, D.; Schulz, A.; Mangelsen, E.; Rothe, M.; Bolbrinker, J.; Kreutz, R. Tissue lipidomic profiling supports a mechanistic role of the prostaglandin E2 pathway for albuminuria development in glomerular hyperfiltration. Front. Netw. Physiol.; 2023; 3, 1271042. [DOI: https://dx.doi.org/10.3389/fnetp.2023.1271042] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38205443]

54. Ko, D.S.; Kang, J.; Heo, H.J.; Kim, E.K.; Kim, K.; Kang, J.M.; Jung, Y.; Baek, S.E.; Kim, Y.H. Role of PCK2 in the proliferation of vascular smooth muscle cells in neointimal hyperplasia. Int. J. Biol. Sci.; 2022; 18, pp. 5154-5167. [DOI: https://dx.doi.org/10.7150/ijbs.75577] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35982907]

55. Li, Y.L.H.; Gao-Li, J.; Parlakian, A.; Agbulut, O.; Lacolley, P.; Regnault, V.; Li, Z. Role of TGF-β co-receptor CD109 in extracellular matrix production of smooth muscle cells. Arch. Cardiovasc. Dis. Suppl.; 2018; 10, pp. 247-248.

56. Urbonaviciene, G.; Frystyk, J.; Urbonavicius, S.; Lindholt, J.S. IGF-I and IGFBP2 in peripheral artery disease: Results of a prospective study. Scand. Cardiovasc. J.; 2014; 48, pp. 99-105. [DOI: https://dx.doi.org/10.3109/14017431.2014.891760] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24548188]

57. Wang, W.; Yu, K.; Zhao, S.Y.; Mo, D.G.; Liu, J.H.; Han, L.J.; Li, T.; Yao, H.C. The impact of circulating IGF-1 and IGFBP-2 on cardiovascular prognosis in patients with acute coronary syndrome. Front. Cardiovasc. Med.; 2023; 10, 1126093. [DOI: https://dx.doi.org/10.3389/fcvm.2023.1126093] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36970368]

58. Schumacher, T.; Benndorf, R.A. ABC Transport Proteins in Cardiovascular Disease-A Brief Summary. Molecules; 2017; 22, 589. [DOI: https://dx.doi.org/10.3390/molecules22040589]

59. Gumus, E.; Ozen, H. Glycogen storage diseases: An update. World J. Gastroenterol.; 2023; 29, pp. 3932-3963. [DOI: https://dx.doi.org/10.3748/wjg.v29.i25.3932] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37476587]

60. Lyon, C.A.; Johnson, J.L.; Williams, H.; Sala-Newby, G.B.; George, S.J. Soluble N-cadherin overexpression reduces features of atherosclerotic plaque instability. Arterioscler. Thromb. Vasc. Biol.; 2009; 29, pp. 195-201. [DOI: https://dx.doi.org/10.1161/ATVBAHA.108.178087]

61. Huang, Z.; Shen, S.; Han, X.; Li, W.; Luo, W.; Lin, L.; Xu, M.; Wang, Y.; Huang, W.; Wu, G. et al. Macrophage DCLK1 promotes atherosclerosis via binding to IKKbeta and inducing inflammatory responses. EMBO Mol. Med.; 2023; 15, e17198. [DOI: https://dx.doi.org/10.15252/emmm.202217198] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36896602]

62. Dubourg, V.; Schwerdt, G.; Schreier, B.; Kopf, M.; Mildenberger, S.; Benndorf, R.A.; Gekle, M. EGFR activation differentially affects the inflammatory profiles of female human aortic and coronary artery endothelial cells. Sci. Rep.; 2023; 13, 22827. [DOI: https://dx.doi.org/10.1038/s41598-023-50148-7]

63. Kurdi, A.; Martinet, W.; De Meyer, G.R.Y. mTOR Inhibition and Cardiovascular Diseases: Dyslipidemia and Atherosclerosis. Transplantation; 2018; 102, (Suppl. S1), pp. S44-S46. [DOI: https://dx.doi.org/10.1097/TP.0000000000001693] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28230638]

64. Stotland, A.B.; Spivia, W.; Orosco, A.; Andres, A.M.; Gottlieb, R.A.; Van Eyk, J.E.; Parker, S.J. MitoPlex: A targeted multiple reaction monitoring assay for quantification of a curated set of mitochondrial proteins. J. Mol. Cell Cardiol.; 2020; 142, pp. 1-13. [DOI: https://dx.doi.org/10.1016/j.yjmcc.2020.03.011] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32234390]

65. Karger, A.B.; Steffen, B.T.; Nomura, S.O.; Guan, W.; Garg, P.K.; Szklo, M.; Budoff, M.J.; Tsai, M.Y. Association Between Homocysteine and Vascular Calcification Incidence, Prevalence, and Progression in the MESA Cohort. J. Am. Heart Assoc.; 2020; 9, e013934. [DOI: https://dx.doi.org/10.1161/JAHA.119.013934] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32013703]

66. Scaglione, F.; Panzavolta, G. Folate, folic acid and 5-methyltetrahydrofolate are not the same thing. Xenobiotica; 2014; 44, pp. 480-488. [DOI: https://dx.doi.org/10.3109/00498254.2013.845705]

67. Touam, M.; Zingraff, J.; Jungers, P.; Chadefaux-Vekemans, B.; Drueke, T.; Massy, Z.A. Effective correction of hyperhomocysteinemia in hemodialysis patients by intravenous folinic acid and pyridoxine therapy. Kidney Int.; 1999; 56, pp. 2292-2296. [DOI: https://dx.doi.org/10.1046/j.1523-1755.1999.00792.x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/10594808]

68. Di Minno, A.; Turnu, L.; Porro, B.; Squellerio, I.; Cavalca, V.; Tremoli, E.; Di Minno, M.N. 8-Hydroxy-2-Deoxyguanosine Levels and Cardiovascular Disease: A Systematic Review and Meta-Analysis of the Literature. Antioxid. Redox Signal; 2016; 24, pp. 548-555. [DOI: https://dx.doi.org/10.1089/ars.2015.6508]

69. Welsh, D.G.; Brayden, J.E. Mechanisms of coronary artery depolarization by uridine triphosphate. Am. J. Physiol. Heart Circ. Physiol.; 2001; 280, pp. H2545-H2553. [DOI: https://dx.doi.org/10.1152/ajpheart.2001.280.6.H2545]

70. Wang, Y.K.; Li, S.J.; Zhou, L.L.; Li, D.; Guo, L.W. GALNT3 protects against vascular calcification by reducing oxidative stress and apoptosis of smooth muscle cells. Eur. J. Pharmacol.; 2023; 939, 175447. [DOI: https://dx.doi.org/10.1016/j.ejphar.2022.175447] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36473594]

71. Lin, P.T.; Cheng, C.H.; Liaw, Y.P.; Lee, B.J.; Lee, T.W.; Huang, Y.C. Low pyridoxal 5′-phosphate is associated with increased risk of coronary artery disease. Nutrition; 2006; 22, pp. 1146-1151. [DOI: https://dx.doi.org/10.1016/j.nut.2006.08.013]

72. Yu, F.; Liu, F.; Luo, J.Y.; Zhao, Q.; Wang, H.L.; Fang, B.B.; Li, X.M.; Yang, Y.N. Targeted activation of ERK1/2 reduces ischemia and reperfusion injury in hyperglycemic myocardium by improving mitochondrial function. Ann. Transl. Med.; 2022; 10, 1238. [DOI: https://dx.doi.org/10.21037/atm-22-5149] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36544682]

73. Malik, K.U.; Ryan, P.; McGiff, J.C. Modification by prostaglandins E1 and E2, indomethacin, and arachidonic acid of the vasoconstrictor responses of the isolated perfused rabbit and rat mesenteric arteries to adrenergic stimuli. Circ. Res.; 1976; 39, pp. 163-168. [DOI: https://dx.doi.org/10.1161/01.RES.39.2.163] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/181162]

74. Sato, A.; Watanabe, H.; Yamazaki, M.; Sakurai, E.; Ebina, K. Estrogen Sulfotransferase is Highly Expressed in Vascular Endothelial Cells Overlying Atherosclerotic Plaques. Protein J.; 2022; 41, pp. 179-188. [DOI: https://dx.doi.org/10.1007/s10930-022-10042-z]

75. Hartman, R.J.G.; Owsiany, K.; Ma, L.; Koplev, S.; Hao, K.; Slenders, L.; Civelek, M.; Mokry, M.; Kovacic, J.C.; Pasterkamp, G. et al. Sex-Stratified Gene Regulatory Networks Reveal Female Key Driver Genes of Atherosclerosis Involved in Smooth Muscle Cell Phenotype Switching. Circulation; 2021; 143, pp. 713-726. [DOI: https://dx.doi.org/10.1161/CIRCULATIONAHA.120.051231] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33499648]

76. Zhou, C.; Lin, Z.; Cao, H.; Chen, Y.; Li, J.; Zhuang, X.; Ma, D.; Ji, L.; Li, W.; Xu, S. et al. Anxa1 in smooth muscle cells protects against acute aortic dissection. Cardiovasc. Res.; 2022; 118, pp. 1564-1582. [DOI: https://dx.doi.org/10.1093/cvr/cvab109] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33757117]

77. Purvis, G.S.D.; Solito, E.; Thiemermann, C. Annexin-A1: Therapeutic Potential in Microvascular Disease. Front. Immunol.; 2019; 10, 938. [DOI: https://dx.doi.org/10.3389/fimmu.2019.00938] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31114582]

78. Mendez-Barbero, N.; San Sebastian-Jaraba, I.; Blazquez-Serra, R.; Martin-Ventura, J.L.; Blanco-Colio, L.M. Annexins and cardiovascular diseases: Beyond membrane trafficking and repair. Front. Cell Dev. Biol.; 2022; 10, 1000760. [DOI: https://dx.doi.org/10.3389/fcell.2022.1000760] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36313572]

79. Cheng, L.; Wang, H.; Maboh, R.; Mao, G.; Wu, X.; Chen, H. LncRNA LINC00281/Annexin A2 Regulates Vascular Smooth Muscle Cell Phenotype Switching via the Nuclear Factor-Kappa B Signaling Pathway. J. Cardiovasc. Transl. Res.; 2022; 15, pp. 971-984. [DOI: https://dx.doi.org/10.1007/s12265-022-10242-z]

80. Sikorski, K.; Czerwoniec, A.; Bujnicki, J.M.; Wesoly, J.; Bluyssen, H.A. STAT1 as a novel therapeutical target in pro-atherogenic signal integration of IFNgamma, TLR4 and IL-6 in vascular disease. Cytokine Growth Factor Rev.; 2011; 22, pp. 211-219. [DOI: https://dx.doi.org/10.1016/j.cytogfr.2011.06.003] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21752694]

81. Martin-Lorenzo, M.; Gonzalez-Calero, L.; Maroto, A.S.; Martinez, P.J.; Zubiri, I.; de la Cuesta, F.; Mourino-Alvarez, L.; Barderas, M.G.; Heredero, A.; Aldamiz-Echevarria, G. et al. Cytoskeleton deregulation and impairment in amino acids and energy metabolism in early atherosclerosis at aortic tissue with reflection in plasma. Biochim. Biophys. Acta; 2016; 1862, pp. 725-732. [DOI: https://dx.doi.org/10.1016/j.bbadis.2015.12.006] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26675529]

82. Di Taranto, M.D.; Morgante, A.; Bracale, U.M.; D’Armiento, F.P.; Porcellini, M.; Bracale, G.; Fortunato, G.; Salvatore, F. Altered expression of inflammation-related genes in human carotid atherosclerotic plaques. Atherosclerosis; 2012; 220, pp. 93-101. [DOI: https://dx.doi.org/10.1016/j.atherosclerosis.2011.10.022] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22112851]

83. Ariyasinghe, N.R.; de Souza Santos, R.; Gross, A.; Aghamaleky-Sarvestany, A.; Kreimer, S.; Escopete, S.; Parker, S.J.; Sareen, D. Proteomics of novel induced pluripotent stem cell-derived vascular endothelial cells reveal extensive similarity with an immortalized human endothelial cell line. Physiol. Genom.; 2023; 55, pp. 324-337. [DOI: https://dx.doi.org/10.1152/physiolgenomics.00166.2022]

84. Puchenkova, O.A.; Soldatov, V.O.; Belykh, A.E.; Bushueva, O.; Piavchenko, G.A.; Venediktov, A.A.; Shakhpazyan, N.K.; Deykin, A.V.; Korokin, M.V.; Pokrovskiy, M.V. Cytokines in Abdominal Aortic Aneurysm: Master Regulators with Clinical Application. Biomark. Insights; 2022; 17, 11772719221095676. [DOI: https://dx.doi.org/10.1177/11772719221095676]

85. Larionov, S.; Dedeck, O.; Birkenmeier, G.; Thal, D.R. Expression of alpha2-macroglobulin, neutrophil elastase, and interleukin-1alpha differs in early-stage and late-stage atherosclerotic lesions in the arteries of the circle of Willis. Acta Neuropathol.; 2007; 113, pp. 33-43. [DOI: https://dx.doi.org/10.1007/s00401-006-0134-0]

86. Buso, G.; Faggin, E.; Bressan, A.; Galliazzo, S.; Cinetto, F.; Felice, C.; Fusaro, M.; Erdmann, A.; Pauletto, P.; Rattazzi, M. et al. Biomarkers of Neutrophil Activation in Patients with Symptomatic Chronic Peripheral Artery Disease Predict Worse Cardiovascular Outcome. Biomedicines; 2023; 11, 866. [DOI: https://dx.doi.org/10.3390/biomedicines11030866]

87. Quaye, L.N.K.; Dalzell, C.E.; Deloukas, P.; Smith, A.J.P. The Genetics of Coronary Artery Disease: A Vascular Perspective. Cells; 2023; 12, 2232. [DOI: https://dx.doi.org/10.3390/cells12182232] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37759455]

88. Wang, Y.; Nanda, V.; Direnzo, D.; Ye, J.; Xiao, S.; Kojima, Y.; Howe, K.L.; Jarr, K.U.; Flores, A.M.; Tsantilas, P. et al. Clonally expanding smooth muscle cells promote atherosclerosis by escaping efferocytosis and activating the complement cascade. Proc. Natl. Acad. Sci. USA; 2020; 117, pp. 15818-15826. [DOI: https://dx.doi.org/10.1073/pnas.2006348117]

89. Adams, G.N.; Stavrou, E.X.; Fang, C.; Merkulova, A.; Alaiti, M.A.; Nakajima, K.; Morooka, T.; Merkulov, S.; Larusch, G.A.; Simon, D.I. et al. Prolylcarboxypeptidase promotes angiogenesis and vascular repair. Blood; 2013; 122, pp. 1522-1531. [DOI: https://dx.doi.org/10.1182/blood-2012-10-460360]

90. Wang, Y.; Zhang, L.; Chen, H.; Yang, J.; Cui, Y.; Wang, H. Coronary artery disease-associated immune gene RBP1 and its pan-cancer analysis. Front. Cardiovasc. Med.; 2023; 10, 1091950. [DOI: https://dx.doi.org/10.3389/fcvm.2023.1091950]

91. Liu, J.; Wu, J.; Li, L.; Li, T.; Wang, J. The Role of Exosomal Non-Coding RNAs in Coronary Artery Disease. Front. Pharmacol.; 2020; 11, 603104. [DOI: https://dx.doi.org/10.3389/fphar.2020.603104] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33363474]

92. Hsieh, D.; Hsieh, A.; Stea, B.; Ellsworth, R. IGFBP2 promotes glioma tumor stem cell expansion and survival. Biochem. Biophys. Res. Commun.; 2010; 397, pp. 367-372. [DOI: https://dx.doi.org/10.1016/j.bbrc.2010.05.145] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20515648]

93. Petazzi, P.; Ventura, T.; Luongo, F.P.; McClafferty, H.; May, A.; Taylor, H.A.; Shipston, M.J.; Romanò, N.; Forrester, L.M.; Menéndez, P. et al. A novel human pluripotent stem cell-based gene activation system identifies IGFBP2 as a mediator in the production of hematopoietic progenitors in vitro. eLife; 2024; preprints.

94. Yau, S.W.; Azar, W.J.; Sabin, M.A.; Werther, G.A.; Russo, V.C. IGFBP-2—Taking the lead in growth, metabolism and cancer. J. Cell Commun. Signal; 2015; 9, pp. 125-142. [DOI: https://dx.doi.org/10.1007/s12079-015-0261-2]

95. Yang, L.; Geng, Z.; Nickel, T.; Johnson, C.; Gao, L.; Dutton, J.; Hou, C.; Zhang, J. Differentiation of Human Induced-Pluripotent Stem Cells into Smooth-Muscle Cells: Two Novel Protocols. PLoS ONE; 2016; 11, e0147155. [DOI: https://dx.doi.org/10.1371/journal.pone.0147155]

96. Talebizadeh, Z.; Simon, S.D.; Butler, M.G. X chromosome gene expression in human tissues: Male and female comparisons. Genomics; 2006; 88, pp. 675-681. [DOI: https://dx.doi.org/10.1016/j.ygeno.2006.07.016]

97. Hartman, R.; Siemelink, M.; Haitjema, S.; Dekkers, K.F.; Slenders, L.; Boltjes, A.; Mokry, M.; Timmerman, N.; De Borst, G.J.; Heijmans, B.T. et al. Sex-dependent gene regulation of human atherosclerotic plaques by DNA methylation and transcriptome integration points to smooth muscle cell involvement in women. Atherosclerosis; 2021; 331, e217. [DOI: https://dx.doi.org/10.1016/j.atherosclerosis.2021.06.665]

98. Women and Heart Disease. 2024; Available online: https://www.heartfoundation.org.au/your-heart/heart-conditions-in-women (accessed on 1 May 2024).

99. Head, T.; Daunert, S.; Goldschmidt-Clermont, P.J. The Aging Risk and Atherosclerosis: A Fresh Look at Arterial Homeostasis. Front. Genet.; 2017; 8, 216. [DOI: https://dx.doi.org/10.3389/fgene.2017.00216]

100. Lo Sardo, V.; Ferguson, W.; Erikson, G.A.; Topol, E.J.; Baldwin, K.K.; Torkamani, A. Influence of donor age on induced pluripotent stem cells. Nat. Biotechnol.; 2017; 35, pp. 69-74. [DOI: https://dx.doi.org/10.1038/nbt.3749]

101. Scesa, G.; Adami, R.; Bottai, D. iPSC Preparation and Epigenetic Memory: Does the Tissue Origin Matter?. Cells; 2021; 10, 1470. [DOI: https://dx.doi.org/10.3390/cells10061470]

102. Crews, J.K.; Khalil, R.A. Gender-specific inhibition of Ca2+ entry mechanisms of arterial vasoconstriction by sex hormones. Clin. Exp. Pharmacol. Physiol.; 1999; 26, pp. 707-715. [DOI: https://dx.doi.org/10.1046/j.1440-1681.1999.03110.x]

103. Crews, J.K.; Khalil, R.A. Antagonistic effects of 17 beta-estradiol, progesterone, and testosterone on Ca2+ entry mechanisms of coronary vasoconstriction. Arterioscler. Thromb. Vasc. Biol.; 1999; 19, pp. 1034-1040. [DOI: https://dx.doi.org/10.1161/01.ATV.19.4.1034] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/10195933]

104. Jiang, C.; Poole-Wilson, P.A.; Sarrel, P.M.; Mochizuki, S.; Collins, P.; MacLeod, K.T. Effect of 17 beta-oestradiol on contraction, Ca2+ current and intracellular free Ca2+ in guinea-pig isolated cardiac myocytes. Br. J. Pharmacol.; 1992; 106, pp. 739-745. [DOI: https://dx.doi.org/10.1111/j.1476-5381.1992.tb14403.x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/1504758]

105. Thompson, J.; Khalil, R.A. Gender differences in the regulation of vascular tone. Clin. Exp. Pharmacol. Physiol.; 2003; 30, pp. 1-15. [DOI: https://dx.doi.org/10.1046/j.1440-1681.2003.03790.x]

106. Demichev, V.; Messner, C.B.; Vernardis, S.I.; Lilley, K.S.; Ralser, M. DIA-NN: Neural networks and interference correction enable deep proteome coverage in high throughput. Nat. Methods; 2020; 17, pp. 41-44. [DOI: https://dx.doi.org/10.1038/s41592-019-0638-x]

107. Sundararaman, N.; Bhat, A.; Venkatraman, V.; Binek, A.; Dwight, Z.; Ariyasinghe, N.R.; Escopete, S.; Joung, S.Y.; Cheng, S.; Parker, S.J. et al. BIRCH: An Automated Workflow for Evaluation, Correction, and Visualization of Batch Effect in Bottom-Up Mass Spectrometry-Based Proteomics Data. J. Proteome Res.; 2023; 22, pp. 471-481. [DOI: https://dx.doi.org/10.1021/acs.jproteome.2c00671]

108. Tyanova, S.; Cox, J. Perseus: A Bioinformatics Platform for Integrative Analysis of Proteomics Data in Cancer Research. Methods Mol. Biol.; 2018; 1711, pp. 133-148. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29344888]

109. Heberle, H.; Meirelles, G.V.; da Silva, F.R.; Telles, G.P.; Minghim, R. InteractiVenn: A web-based tool for the analysis of sets through Venn diagrams. BMC Bioinform.; 2015; 16, 169. [DOI: https://dx.doi.org/10.1186/s12859-015-0611-3]

110. Sundararaman, N.; Go, J.; Robinson, A.E.; Mato, J.M.; Lu, S.C.; Van Eyk, J.E.; Venkatraman, V. PINE: An Automation Tool to Extract and Visualize Protein-Centric Functional Networks. J. Am. Soc. Mass. Spectrom.; 2020; 31, pp. 1410-1421. [DOI: https://dx.doi.org/10.1021/jasms.0c00032]

111. Xie, Z.; Bailey, A.; Kuleshov, M.V.; Clarke, D.J.B.; Evangelista, J.E.; Jenkins, S.L.; Lachmann, A.; Wojciechowicz, M.L.; Kropiwnicki, E.; Jagodnik, K.M. et al. Gene Set Knowledge Discovery with Enrichr. Curr. Protoc.; 2021; 1, e90. [DOI: https://dx.doi.org/10.1002/cpz1.90] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33780170]

112. Consortium, T.U. UniProt: The Universal Protein Knowledgebase in 2023. Nucleic Acids Res.; 2022; 51, pp. D523-D531. [DOI: https://dx.doi.org/10.1093/nar/gkac1052] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36408920]

113. Pang, Z.; Xu, L.; Viau, C.; Lu, Y.; Salavati, R.; Basu, N.; Xia, J. MetaboAnalystR 4.0: A unified LC-MS workflow for global metabolomics. Nat. Commun.; 2024; 15, 3675. [DOI: https://dx.doi.org/10.1038/s41467-024-48009-6]

114. Kohler, D.; Staniak, M.; Tsai, T.H.; Huang, T.; Shulman, N.; Bernhardt, O.M.; MacLean, B.X.; Nesvizhskii, A.I.; Reiter, L.; Sabido, E. et al. MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale. J. Proteome Res.; 2023; 22, pp. 1466-1482. [DOI: https://dx.doi.org/10.1021/acs.jproteome.2c00834] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37018319]

Word count: 12511

Show less

© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

The prevalence of cardiovascular disease varies with sex, and the impact of intrinsic sex-based differences on vasculature is not well understood. Animal models can provide important insights into some aspects of human biology; however, not all discoveries in animal systems translate well to humans. To explore the impact of chromosomal sex on proteomic phenotypes, we used iPSC-derived vascular smooth muscle cells from healthy donors of both sexes to identify sex-based proteomic differences and their possible effects on cardiovascular pathophysiology. Our analysis confirmed that differentiated cells have a proteomic profile more similar to healthy primary aortic smooth muscle cells than iPSCs. We also identified sex-based differences in iPSC-derived vascular smooth muscle cells in pathways related to ATP binding, glycogen metabolic process, and cadherin binding as well as multiple proteins relevant to cardiovascular pathophysiology and disease. Additionally, we explored the role of autosomal and sex chromosomes in protein regulation, identifying that proteins on autosomal chromosomes also show sex-based regulation that may affect the protein expression of proteins from autosomal chromosomes. This work supports the biological relevance of iPSC-derived vascular smooth muscle cells as a model for disease, and further exploration of the pathways identified here can lead to the discovery of sex-specific pharmacological targets for cardiovascular disease.

Details

Title
Identification of Disease-Relevant, Sex-Based Proteomic Differences in iPSC-Derived Vascular Smooth Muscle Cells
Author
Ariyasinghe, Nethika R 1   VIAFID ORCID Logo  ; Gupta, Divya 1 ; Escopete, Sean 1 ; Rai, Deepika 1   VIAFID ORCID Logo  ; Stotland, Aleksandr 1   VIAFID ORCID Logo  ; Niveda Sundararaman 1 ; Ngu, Benjamin 2 ; Dabke, Kruttika 3   VIAFID ORCID Logo  ; McCarthy, Liam 1 ; Santos, Roberta S 4   VIAFID ORCID Logo  ; McCain, Megan L 5 ; Sareen, Dhruv 6 ; Parker, Sarah J 7   VIAFID ORCID Logo 

1  Advanced Clinical Biosystems Research Institute, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; [email protected] (N.R.A.); [email protected] (D.G.); [email protected] (S.E.); [email protected] (D.R.); [email protected] (A.S.); [email protected] (N.S.); [email protected] (L.M.) 
2  Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90007, USA; [email protected] (B.N.); [email protected] (M.L.M.) 
3  Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; [email protected] 
4  Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; [email protected] (R.S.S.); [email protected] (D.S.); Cedars-Sinai Biomanufacturing Center, Cedars-Sinai Medical Center, West Hollywood, CA 90069, USA 
5  Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90007, USA; [email protected] (B.N.); [email protected] (M.L.M.); Department of Stem Cell Biology and Regenerative Medicine, University of Southern California, Los Angeles, CA 90033, USA 
6  Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; [email protected] (R.S.S.); [email protected] (D.S.); Cedars-Sinai Biomanufacturing Center, Cedars-Sinai Medical Center, West Hollywood, CA 90069, USA; iPSC Core, David and Janet Polak Foundation Stem Cell Core Laboratory, Cedars-Sinai Medical Center, West Hollywood, CA 90069, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA 
7  Advanced Clinical Biosystems Research Institute, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; [email protected] (N.R.A.); [email protected] (D.G.); [email protected] (S.E.); [email protected] (D.R.); [email protected] (A.S.); [email protected] (N.S.); [email protected] (L.M.); Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Board of Governors Innovation Center, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA 
First page
187
Publication year
2025
Publication date
2025
Publisher
MDPI AG
ISSN
16616596
e-ISSN
14220067
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.3390/ijms26010187
ProQuest document ID
3153755589
Copyright
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.