The pleiotropic benefits of statins may result from their impact on vascular inflammation. The molecular process underlying this phenomenon is not fully elucidated. In the present study, RNA-sequencing designed to investigate gene expression patterns after CD47–SIRPα inhibition identifies a link of statins, efferocytosis and vascular inflammation. In vivo and in vitro studies provide evidence that statins augment programmed cell removal by inhibiting the nuclear translocation of NF-κB1 p50 and suppressing the expression of the critical ‘don’t-eat-me’ molecule, CD47. Statins amplify the phagocytic capacity of macrophages, and thus the anti-atherosclerotic effects of CD47–SIRPα blockade, in an additive manner. Analyses of clinical biobank specimens suggest a similar link between statins and CD47 expression in humans, highlighting the potential translational implications. Taken together, our findings identify efferocytosis and CD47 as pivotal mediators of statin pleiotropy. In turn, statins amplify the anti-atherosclerotic effects of prophagocytic therapies independently of any lipid-lowering effect.
Atherosclerosis is a lipoprotein-driven, inflammatory process underlying heart attack and stroke and is the leading cause of death worldwide1. In the last four decades, 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors, known as statins, have been established as the drug of first choice for patients with atherosclerotic cardiovascular disease. Striking data from multiple clinical trials have shown that they reduce mortality in both primary and secondary prevention2,3. Statins inhibit the rate-limiting enzyme of cholesterol biosynthesis and reduce serum low-density lipoprotein (LDL)-cholesterol, thus directly addressing one of the most important risk factors for the disease.
However, there is growing evidence that the beneficial properties of statins are not restricted to their influence on LDL. Indeed, the so-called pleiotropic effects of statins are now well described4,5 and may result from their impact on vascular inflammation6,7. This possibility has garnered attention due to a series of recent high-profile studies, including CANTOS (which used a monoclonal antibody directed against the interleukin (IL)-1β pathway) and COLCOT (which used colchicine to target tubulin polymerization, microtubule generation and possibly the inflammasome), which helped definitively prove the ‘inflammatory hypothesis of atherosclerosis’8. These large clinical trials are spurring the concept of targeting inflammation as a way to provide benefit independently of lipid levels9,10.
A critical driver of inflammation is the accumulation of diseased and dying macrophages and smooth muscle cells in the atherosclerotic vessel. Normally, these pathological cells would be identified for phagocytic removal by macrophages in the plaque. In keeping with Peter Henson’s suggestion, the term ‘efferocytosis’ is used to emphasize the relevance of this process (derived from the Latin verb ‘efferre–effero’, meaning to bury, or to carry to the grave). However, this process is defective in atherosclerosis, due in part to the pathological upregulation of a so-called ‘don’t-eat-me’ molecule known as CD4711. The presence of this anti-phagocytic signal has been hypothesized to permit the accumulation of inflammatory debris in the necrotic core and therefore promote lesion expansion. Mechanistically, CD47 binds to signal-regulatory protein α (SIRPα), a transmembrane protein with an immunoreceptor tyrosine-based inhibitor motif domain. Ligation of SIRPα leads to tyrosine phosphorylation and subsequent activation of the Src homology 2 domain-containing phosphatase-1 (SHP-1), which inhibits phagocytosis11. Preclinical studies using antibodies directed against CD47 or nanoparticles targeting SHP-1 led to reactivation of intraplaque efferocytosis and prevention of atherosclerosis, supporting a causal relationship11,12,13. In addition, we provided evidence that a humanized anti-CD47 antibody could reduce arterial [18F]fluorodeoxyglucose uptake, detected by positron emission tomography and computed tomography, and thus suppress vascular inflammation in patients14. Although these preliminary observations require confirmation in a prospective trial, pro-efferocytic therapies are hypothesized to represent a new strategy to target inflammation in cardiovascular disease.
In the present study, we describe how an unbiased approach designed to investigate gene expression patterns in response to CD47–SIRPα blockade led to a surprising link between statins and efferocytosis. We provide evidence that statins may amplify the anti-atherosclerotic effects of CD47–SIRPα blockade, due to a nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB1)-dependent alteration in ‘don’t-eat-me’ molecule expression, which occurs independent of any effect on lipid levels. When coupled with supporting clinical data from patients with atherosclerotic cardiovascular disease, these findings provide insights not only into the mechanistic origins of statin pleiotropy, but also into a basis for future translational efforts focused on therapies that may synergize with evidence-based medicines to further reduce the risk of major adverse clinical events.
Statin as top upstream regulator of SHP-1 inhibition
As an unbiased approach, RNA-sequencing (RNA-seq) was used to examine the transcriptome of macrophages after CD47–SIRPα axis blockade. Therefore, bone marrow-derived macrophages were incubated with single-walled carbon nanotubes loaded with fluorescent Cy5.5 probe (referred to as SWNT) or SWNT loaded with a small-molecule inhibitor of SIRPα’s downstream effector molecule SHP-1 (referred to as SHP1i) for 24 h, and sorted by flow cytometry to isolate Cy5.5-positive macrophages in each group, which were then subjected to RNA-seq (Extended Data Fig. 1a). In the present study, we identified 128 differentially expressed genes with a false discovery rate (FDR) <0.10 (19 upregulated and 109 downregulated; Fig. 1a and Extended Data Table 1).
We next identified ‘upstream regulators’ using QIAGEN’s ingenuity pathway analysis (IPA). Intriguingly, lovastatin, a first-generation HMG-CoA reductase inhibitor, was one of the top activated upstream regulators and the only drug in the database (z-score 2.184), based on the relevant regulation of apolipoprotein E (ApoE), ras homolog family member B (Rhob), RB transcriptional corepressor-like 1 (Rbl1), glutathione peroxidase 3 (Gpx3) and X-linked inhibitor of apoptosis (Xiap) (Fig. 1b,c and Extended Data Table 2). In anticipation of our in vivo model, we validated these findings for atorvastatin, the most widely prescribed statin with one of the most favorable safety profiles15, by quantitative (q)PCR. We found similar gene expression changes on atorvastatin treatment (Extended Data Fig. 1b). In conclusion, these data suggested an unexpected overlap of HMG-CoA reductase inhibition and CD47–SIRPα blockade.
Effect of CD47 blockade and atorvastatin on atherosclerosis
The unanticipated crosstalk and the resulting emerging translational potential prompted us to test whether combined treatment of CD47–SIRPα blockade and HMG-CoA reductase inhibition has additive effects on the atherosclerotic plaque activity in vivo. To address this question, high-fat, diet-fed apolipoprotein E-deficient (ApoE−/−) mice received therapy with atorvastatin alone or in combination with CD47–SIRPα blockade (Extended Data Fig. 2a–i). The latter was achieved by targeting either CD47 (using anti-CD47 antibodies11) or SIRPα’s downstream effector molecule SHP-1 (using SHP1i13). Intriguingly, combined treatment not only decreased lesion size but also reduced the necrotic core area (Fig. 2a,b and Extended Data Fig. 2j). This is important to note, given that the necrotic core is thought to be a key driver for plaque vulnerability in lesions and thus for acute vascular events16. Of note, there were no notable differences in plasma cholesterol and blood glucose between the cohorts (Fig. 2c). Subsequently, we used the single treatment cohorts to determine additivity/synergy of compounds (Extended Data Fig. 2k,l). Applying the Bliss independence model (formula Ec = Ea + Eb – Ea × Eb, where Ec is the combined effect produced by the combination of compounds a and b)17 on the analyses of lesion area and necrotic core size, we computed an additive anti-atherosclerotic effect for both parameters in vivo (Fig. 2d,e). Together, these observations suggested an additive therapeutic effect on combination.
Effect of CD47 blockade and atorvastatin on efferocytosis
Given that the efficient clearance of apoptotic cells, a process called efferocytosis, is impaired in atherosclerosis18, we reasoned that atorvastatin might increase the efferocytosis rate and thus beneficially impact lesion development. To test this hypothesis, we first employed an in vitro phagocytosis assay using macrophages as phagocytes and target cells. We observed, using flow cytometry, a relevant increase in the efferocytic rate of apoptotic cells on combined treatment (atorvastatin + SHP1i) compared with single therapies (Fig. 3a and Extended Data Fig. 3a). Of note, the Bliss independence model confirmed additivity (Fig. 3b). As previously shown, inhibition of the CD47–SIRPα axis (using anti-CD47 antibodies11 or SHP1i13) did not alter the rate of programmed cell death quantified by caspase-3/7 activity in our cells. Similarly, we did not find an effect on apoptosis by atorvastatin or combined treatment strategies (Fig. 3c and Extended Data Fig. 3b), suggesting an enhancement of efferocytosis without altering apoptosis.
To determine the relevance of these observations in vivo, we also investigated the cleaved caspase-3 activity and the number of ‘free’ apoptotic bodies not associated with an intraplaque macrophage, both reliable measures of accumulation of apoptotic bodies and thus efferocytosis in tissue specimens. In agreement with our in vitro observations, we found a decrease in the number of apoptotic bodies in the lesion with the combined treatment, as suggested by our immunofluorescence studies (Fig. 3d,e and Extended Data Fig. 3c,d). Again, the Bliss independence model demonstrated additivity (Fig. 3f). Taken together, these data suggested that the combination of HMG-CoA reductase inhibition and CD47–SIRPα blockade markedly increased the efferocytosis rate and may thus explain the additive effect on atherosclerotic plaque activity.
Atorvastatin directly regulated gene expression of Cd47
Having identified the efferocytic rate as a pivotal link for additivity of combined treatment, we sought to elucidate the underlying mechanism. Given the critical emerging role of the key ‘don’t-eat-me’ molecule CD47 in atherosclerosis and efferocytosis, we hypothesized that there might be a direct effect of atorvastatin on CD47 expression. To answer this question, we investigated the Cd47 expression in two of the major cellular components of atherosclerosis, smooth muscle cells and macrophages. Stimulation with tumor necrosis factor-α (TNF-α) increased Cd47 expression, but it is of interest that this effect was more pronounced in smooth muscle cells compared with macrophages. Consequently, treatment with atorvastatin resulted in a larger reduction of Cd47 on both RNA and protein levels in smooth muscle cells (Fig. 4a–d and Extended Data Fig. 4a–c).
To directly link atorvastatin treatment with Cd47 expression in smooth muscle cells, we used a dual luciferase reporter assay, which quantified the relative change to basal values obtained from control-transfected cells. We observed that atorvastatin was able to inhibit the TNF-α-induced Cd47 promoter activity (Fig. 4e). Based on our previous studies11, we hypothesized that this treatment might reduce the nuclear translocation of NF-κB1 p50, which is a known key transcriptional factor for CD47. In alignment with this hypothesis, we found that atorvastatin inhibited the nuclear translocation of NF-κB1 p50. Importantly, the effect was eliminated with the addition of mevalonate, an antagonist to atorvastatin (Fig. 4f,g and Extended Data Fig. 4d). Although additional causation studies are warranted, these results suggested that atorvastatin directly reduced the pathological CD47 upregulation in atherosclerosis via inhibition of the proinflammatory factor NF-κB1 p50. These results may provide a mechanistic understanding for statin’s pleiotropic benefits through their regulation of efferocytosis.
To determine whether HMG-CoA reductase inhibitors result in lower CD47 expression during human atherogenesis, we evaluated carotid endarterectomy samples from the Munich Vascular Biobank. Of note, we found that patients receiving statin treatment had lower CD47 expression than a propensity score (age, gender, medication, symptom and physical status)-matched cohort without such a medication (Fig. 4h). Although the potential for residual confounding exists, these data suggested that HMG-CoA reductase inhibitors reduce the pathological upregulation of CD47 in human atherosclerosis and thus may have additive effects on the efferocytosis rate on combined treatment (Fig. 5).
The present study provides new insights that may help explain the pleiotropic effects of statins. We show that statins augment efferocytosis by inhibiting the nuclear translocation of NF-κB1 p50 and suppressing expression of the key ‘don’t-eat-me’ molecule CD47. We demonstrate that statins amplify the anti-atherosclerotic effects of two recently described pro-efferocytic therapies, and do so independent of any lipid-lowering effect. Analyses of clinical biobank specimens suggest a similar link between statins and CD47 expression in humans, highlighting the potential translational implications of these findings. Together, these results provide a possible mechanism for how statins provide benefit beyond their well-described effect on cholesterol metabolism, highlighting the possibility that they may also reduce atherosclerosis by exerting a prophagocytic and anti-inflammatory effect directly in the vessel wall.
For decades, researchers have had difficulty attributing the outsized clinical benefit associated with statins solely to their impact on LDL levels. For example, patients randomized to intensive statin therapy on hospital admission for an acute coronary syndrome experience a reduction in clinical event rates in the first 16 weeks (MIRACL study)19 or as early as 30 d after the start of therapy (PROVE IT-TIMI 22)20. This early time–benefit window may be ascribed to a wide range of potential anti-inflammatory mechanisms, including reduced C-reactive protein6, T-cell activation and endothelial function modulation via Krüpple-like factor 2 (refs. 21,22), leukocyte–endothelial cell adhesion23 and/or inhibition of prenylation of small G proteins24,25. The fact that atorvastatin restores dysfunctional efferocytosis in atherosclerosis is therefore interesting, given that efferocytosis signaling is thought to occur independently of traditional risk factors, and has been directly linked to inflammation downstream of TNF-α11. As evidence continues to accrue showing that anti-cytokine agents such as canakinumab (IL-1β, CANTOS)9 and ziltivekimab (IL-6, RESCUE)26 may be promising targets in cardiovascular disease, it is tempting to speculate whether the disproportionately powerful effects of statins on atherosclerosis may be mediated, in part, via the clearance of inflammatory cells within the necrotic core.
Although previous studies have suggested that statins can increase the rate of phagocytosis, the mechanism has been elusive to date27,28. In the present study, we demonstrate that atorvastatin is directly linked to the critical ‘don’t-eat-me’ molecule CD47 and thus to the removal of apoptotic debris, supporting a causal relationship. Signaling studies have demonstrated that statins inhibit the nuclear translocation of the inflammatory transcription factor NF-κB1 p50 in vascular cells7,29. As statins are being repurposed for use in other systemic inflammatory disorders, such as rheumatoid arthritis, inflammatory bowel disease and systemic lupus erythematosus30,31,32, it is interesting to speculate that any benefit they might provide could be primarily mediated through effects on the TNF-α–CD47 axis, given that these are lipid-independent diseases.
New therapeutic options are urgently needed in cardiovascular medicine. The finding that pro-efferocytic therapies amplify the pleiotropic benefits of statins in mice is therefore interesting, particularly given our translational observation that statin usage is associated with lower vascular CD47 expression in humans. It is also important to note that this observed benefit occurs independent of classic risk factors such as hypertension, glucose and lipid levels, which is consistent with pevious reports studying these agents in vascular models11,13,33,34,35. As prophagocytic therapies reduce risk irrespective of traditional risk pathways (which can already be addressed with currently available medicines), we hypothesize that reactivating intraplaque efferocytosis is an auspicious target for the residual inflammatory risk in atherosclerosis. Our approach involves targeting either CD47 or SIRPα’s downstream effector molecule, SHP-1. Although we have recently provided evidence that an anti-CD47 antibody may reduce vascular inflammation in humans14, additional dose-ranging studies are needed to address the expected transient anemia induced by anti-CD47 therapy36. Given the observed additive effect of dual treatment, we anticipate that combination therapy with lower doses of anti-CD47 antibodies might maintain full efficacy, while hopefully providing a method to avoid induction of erythrophagocytosis. Although CD47 is ubiquitously expressed (including on red blood cells), its receptor, SIRPα, is largely restricted to myeloid cells, such as macrophages in the atherosclerotic plaque. For this reason, the benefit observed after blockade of SIRPα’s downstream signaling molecule, SHP-1, provides proof of concept for more precise targeting of this axis, and could spur future clinical investigations on anti-SIRPα antibodies that theoretically should also have few, if any, off-target toxicities. Finally, it is worth noting that statins have much more potent lipid-lowering effects in humans than in mice33,34,35. Accordingly, it is tempting to speculate whether the additive benefits of combination therapy observed in the current preclinical study may actually become synergistic when advanced to clinical trials, where concomitant LDL reduction can be expected.
In conclusion, our data show that atorvastatin promotes efferocytosis via a reduction in CD47, leading to a lipid-independent anti-atherosclerotic effect. In addition, the combination of CD47–SIRPα blockade and HMG-CoA reductase inhibition amplifies the phagocytic capacity of macrophages and thus prevents necrotic core expansion in an additive manner. An important area of future research will be to understand which target, CD47 or SIRPα, is the most suitable in humans and which provides the most favorable therapeutic window. If successful, pro-efferocytic strategies could become a new orthogonal therapy on top of guideline-directed medications to further reduce the complications of atherosclerotic vascular disease.
Preparation of single-walled carbon nanotubes
Single-walled carbon nanotubes were prepared as previously reported13. Briefly, raw, high-pressure, catalytically decomposed carbon nanotubes (diameter 0.8–1.2 nm, Unidym) were added to an aqueous solution of 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-polyethylene glycol5000-amine (DSPE-PEG, Nanosoft Polymers, catalog no. 2828-5000), sonicated for at least 1 h and then centrifuged at 100,000g for 1 h to obtain PEGylated single-walled carbon nanotubes. Unbound surfactant was washed by repeated filtration through 100-kDa filters (Millipore Sigma-Aldrich, catalog no. UFC910024). For conjugation of Cy5.5 Mono NHS Ester (GE Healthcare, catalog no. GEPA15601), Cy5.5 Mono NHS Ester was incubated with PEGylated single-walled carbon nanotubes solution (10:1 mole ratio) for 2 h. Excess Cy5.5 dye was removed by five to six rounds of centrifugal filtration until the filtrate became clear (PEGylated single-walled carbon nanotubes loaded with fluorescent Cy5.5 probes are referred to hereafter as SWNT). SWNT concentrations were determined spectrophotometrically with an extinction coefficient of 7.9 × 106 M−1 cm−1 at 808 nm. The small-molecule inhibitor NSC-87877 (Sigma-Aldrich, catalog no. NSC-87877) of SIRPα’s downstream effector molecule SHP-1 was added to stirred SWNT at 4 °C (pH 7.4) overnight to form SWNT and the small-molecule inhibitor (this system is referred to hereafter as SHP1i). After 24 h of stirring, SHP1i was dialyzed for another 24 h next to phosphate-buffered saline (PBS) to remove unbound NSC-87877 molecules. The concentration of the loaded small-molecule inhibitor NSC-87877 was measured using a NanoDrop One (Thermo Fisher Scientific) at its absorption of 320 nm.
Bone marrow-derived macrophages and RNA-seq
Bone marrow cells were isolated from male C57BL/6J mice (The Jackson Laboratory) at the age of 6–8 weeks and differentiated ex vivo to macrophages in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% heat-inactivated fetal bovine serum (FBS), 100 U ml−1 of penicillin and 100 µg ml−1 of streptomycin (HyClone GE HealthCare, SV30010), and 10 ng ml−1 of murine macrophage colony-stimulating factor (M-CSF; Peprotech, catalog no. 315-02, lot no. 0518245) for 7–10 d. After washing cells with prewarmed PBS to remove nonattached cells, the attached primary mouse macrophages were incubated with 100 pM SWNT or SHP1i for 24 h in serum-free medium at 37 °C. After collecting and washing cells twice with 2% FBS–PBS, macrophages were sorted using a FACSAria cell sorter equipped with FACSDiva v.6 software (BD Life Sciences, Stanford Shared FACS Facility). Channel compensations were performed using single-stained UltraComp eBeads (Thermo Fisher Scientific, catalog no. 01-2222-41) or control macrophages. In addition, macrophages were stained with SYTOX Blue (Invitrogen, catalog no. S34837) to discriminate and exclude nonviable cells. Viable cells (SYTOX Blue negative) were sorted with a 100-µm nozzle into populations that were Cy5.5 positive and Cy5.5 negative and collected in 2% FBS–PBS. Then RNA was extracted using the miRNeasy Mini Kit (QIAGEN, catalog no. 217004). In each group (SWNT and SHP1i), three biological replicates were sequenced. The RNA samples were sent to Novogene Co. (Sacramento, CA, USA) for sample quality control, preparation and sequencing. All samples passed quality control. Subsequently, complementary DNA library construction and sequencing were performed for each sample on an Illumina Novaseq 6000 platform with paired-end 150-bp reads. The data quality report is provided in Source Data Fig. 1a. The sequencing data were uploaded to the Galaxy web platform and we used the public server at usegalaxy.org to further analyze the data (v.2.0.1)37. Briefly, quality control of sequencing data was performed using FastQC (v.0.11.8). HISAT2 (v.2.1.0) was used to map the reads to the reference genome (built-in mm10). FeatureCounts was then used to count the number of reads mapped and DESeq2 (v.1.22.1) to generate the list of differentially regulated genes. P values were adjusted for multiple testing using the Benjamini–Hochberg FDR. Pathway and upstream regulator analyses were performed using IPA (QIAGEN). Of note, the overlap P value in Fig. 1b and Extended Data Table 2 measures whether there is an overlap between the dataset genes generated by the RNA-seq and the genes that are regulated by statins (analysed by Fisher’s exact test, IPA).
Animals and diet
All mice were housed in a pathogen-free animal facility and maintained on a 12-h light:dark cycle at 22 °C with free access to food and water. Mice were acclimatized for 1–2 weeks before experiments. A total of 100 male ApoE−/− mice (B6.129P2-Apoetm1Unc/J, 002052) on a C56BL/6J background (The Jackson Laboratory) aged 8 weeks were used for the present study. The animals were assigned to the experimental groups by complete randomization. For earlier power analysis, the following parameters were used based on previous experience with the therapeutic agents11,12,13: α of 0.05; power of 0.80; expected attrition of 10%; and effect size of 1.5 for atorvastatin and 1.2 for anti-CD47 antibodies and SHP1i. Ten animals were allocated to the PBS and atorvastatin groups as well as at least 13 animals to the immunoglobulin (Ig)G, anti-CD47, anti-CD47 + atorvastatin, SWNT, SHP1i and SHP1i + atorvastatin cohorts. Four animals were not included in the results: two were euthanized due to skin lesions and two were excluded owing to poor quality of histopathology. Consequently, 9 animals in the PBS group, 10 in the atorvastatin group, 13 in the IgG group, 13 in the anti-CD47 group, 13 in the anti-CD47 + atorvastatin group, 12 in the SWNT group, 11 in the SHP1i group and 15 in the SHP1i + atorvastatin group were finally analyzed. Of note, the lesion area of SHP1i animals compared with SWNT-treated animals was published in our previous analysis13. After 2 weeks on a high-fat diet (21% anhydrous milk fat, 19% casein and 0.15% cholesterol, Dyets Inc., catalog no. 101511), the indicated treatment was initiated and continued for the ensuing 9 weeks: (1) PBS by daily gavage versus atorvastatin (Lipitor, Pfizer, prescription formulation) at a dose of 10 mg per kg body weight per day by daily gavage12; (2) 200 μg of the inhibitory anti-CD47 antibody (BioXCell, MIAP410, catalog no. BE0283, lot no. 705318N1) intraperitoneally every other day versus 200 µg of the IgG1 isotype control (BioXCell, MOPC-21, catalog no. BE0083, lot no. 619916O1B) intraperitoneally every other day11; or (3) SWNT at a dose of 200 µl of 400 nM intravenously once weekly versus SHP1i at a dose of 200 µl of 400 nM intravenously once weekly13. Animal studies were approved by the Stanford University Administrative Panel on Laboratory Animal Care (protocol no. 27279) and conformed to the National Institutes of Health (NIH) guidelines for the care and use of laboratory animals.
Tissue preparation and histological analyses
Tissue preparation and histological analyses were performed as previously described11,12. After blood sample collection, mice were perfused with PBS via cardiac puncture and then perfusion fixed with 4% phosphate-buffered paraformaldehyde. Blood samples were analyzed by the Stanford Animal Diagnostic Laboratory. The entire aortic arch was carefully collected, embedded in optimal cutting temperature compound (VWR, catalog no. 25608-930), and sectioned using a cryostat (Leica CM 1950). A total of six sections at a 70-µm distance from the base of the aortic root were collected and analyzed from each mouse. Plaque area (in mm2 and percentage of total vessel area) was quantified by Oil-red O staining (Sigma-Aldrich, catalog no. O1516) and determined from the luminal aspect of the blood vessel through the plaque to the internal elastic lamina. Total vessel area was calculated by encircling the external elastic lamina. Necrotic core (in mm2 and percentage of lesion area) was quantified by Masson’s trichrome staining (Richard-Allen Scientific, catalog no. 22-110-648) and defined as the neointimal area devoid of cellular tissue. Owing to the insensitivity to angles at sectioning38, fraction area (and not absolute surface) was chosen for result presentation in Fig. 2. However, all results are reported and presented in Extended Data Fig. 2. For immunofluorescence staining of atherosclerotic lesions, cryosections were blocked using 5% goat serum (Sigma-Aldrich, catalog no. G9023) in PBS. Next, sections were incubated overnight at 4 °C with the following primary antibodies: Mac3 (BD Life Sciences, catalog no. 550292, 1:100) and cleaved caspase-3 (Cell Signaling Technology, catalog no. 9661, 1:200). After extensive washing, sections were incubated with secondary antibodies from Thermo Fisher Scientific: Alexa Fluor-647 goat anti-rat (catalog no. A-21247, lot no. 2119156, 1:250) and Alexa Fluor-488 goat anti-rabbit (catalog no. A11034, lot no. 2110499, 1:250). Counterstaining to visualize nuclei was performed by incubating with DAPI. Histological sections were imaged using a Zeiss Axioplan (equipped with a Nikon camera) or Leica DMi8 microscope (equipped with a Leica DMC4500 color camera). Fluorescence sections were imaged using a Leica DMi8 microscope (equipped with a Leica K5 camera). Sections were analyzed using Image J/FIJI software (v.2.0.0/1.52p, NIH) in a blinded fashion.
Bliss independence model
The Bliss independence model is a well-established method for determining additivity/synergy of compounds. Of note, additivity means that two drugs equal the sum of parts, whereas synergy means that two drugs are greater than the sum of parts. The formula Ec = Ea + Eb – Ea × Eb, where Ec is the combined effect produced by the combination of compounds a and b, describes how a combination of compounds should act if ‘Bliss additivity’ exists17. To explore the data, we randomly shuffled the results of the single treatment groups using GraphPad random list generator. Then, we calculated Ec for each pair (referred to hereafter as Ecalculated) and compared these results with the observed results in the combined treated cohort (referred to hereafter as Eobserved). A nonsignificant P value was considered to denote additivity.
Primary bone marrow-derived macrophages were grown in DMEM growth medium (Thermo Fisher Scientific, catalog no. 11995-065) supplemented with 10% heat-inactivated FBS (Thermo Fisher Scientific, catalog no. SH3007103HI), 100 U ml−1 of penicillin and 100 µg ml−1 of streptomycin (HyClone GE HealthCare, catalog no. SV30010). Mouse aortic vascular smooth muscle cells (Cell Biologics, catalog no. C57-6080, lot no. M120919W12) were cultured and maintained according to the manufacturer’s instructions. All cells were cultured in a humidified 5% CO2 incubator at 37 °C. The cells were authenticated and tested for bacteria, yeast, fungi and Mycoplasma contamination by the supplier. In addition, samples from vascular smooth muscle cells, bone marrow-derived macrophages and RAW 264.7 macrophages were collected to rule out Mycoplasma contamination by PCR (SouthernBiotech, catalog no. 13100-01; Extended Data Fig. 1d) and/or biochemical detection (Lonza, catalog nos. LT07-118 and LT07-518; Extended Data Fig. 1e). Then, 5 µl of the PCR product was mixed with Bromophenol Blue-loading buffer and separated in a 1.5% DNA-gel electrophoresis using the GeneRuler 1-kb DNA Ladder (Thermo Fisher Scientific, catalog no. SM0313). The following stimuli were applied to the cells in the experiments described below: atorvastatin (Sigma-Aldrich, catalog no. PZ001, source no. 0000040035, batch no. 0000079529), dimethyl sulfoxide (DMSO, sterile; Sigma-Aldrich, catalog no. D2650), recombinant mouse TNF-α (amino acids 80–235, R&D Systems, catalog no. 410-MT, lot no. CS1419081), dl-mevalonic acid 5-phosphate (Sigma-Aldrich, catalog no. 79849, lot no. BCBT1529), staurosporine (Sigma-Aldrich, catalog no. S4400), anti-CD47 antibody (BioXCell, MIAP410, catalog no. BE0283, lot no. 792420D1) and IgG1 control (BioXCell, MOPC-21, catalog no. BE0083, lot no. 722919A2). When atorvastatin was used, equal concentrations of DMSO were added to all respective controls. Of note, the final concentration (v:v) of DMSO was ≤0.1 % to avoid toxic effects.
In vitro phagocytosis assay
Standard in vitro phagocytosis assays were performed using RAW 264.7 macrophages as phagocytes and target cells. Phagocytes were treated with 10 µM atorvastatin, 4 nM SHP1i and equal concentrations of their respective controls (DMSO, SWNT) for 24 h (in detail: ‘vehicle’ = SWNT + DMSO; ‘statin’ = SWNT + atorvastatin; ‘SHP1i’ = SHP1i + DMSO; and ‘SHP1i + statin’ = SHP1i + atorvastatin). Apoptosis in target cells was induced by 1 µM staurosporine for 4 h at 37 °C. Target cells were labeled with 1.25 µM CellTracker Orange CMRA Dye (Thermo Fisher Scientific, catalog no. C34551) or 0.1 µM CellTracker Green CMFDA Dye (Thermo Fisher Scientific, catalog no. C2925) according to the manufacturer’s instructions. Phagocytes were detected by Cy5.5 or 0.3 µM CellTracker Deep Red Dye (Thermo Fisher Scientific, catalog no. C34565). Phagocytes and target cells were then cocultured for 2 h at 37 °C. Double-positive cells (phagocytes = Cy5.5 or Deep Red-positive, target cells = Orange or Green positive) were quantified using the LSRII equipped with FACSDiva v.6 software (BD Life Sciences, Stanford Shared FACS Facility) and analyzed using FlowJo 10.7.1 (BD Life Sciences). Efferocytosis rate was defined as Q2 (double-positive cells) divided by the sum of Q1 and Q2 (total number of apoptotic cells).
The apoptosis assay was performed as previously described11. To evaluate apoptosis, the luminometric Caspase-Glo 3/7 Assay System (Promega, catalog no. G8091) was performed on cultured murine RAW 264.7 macrophages, according to the manufacturer’s protocol. Cells were seeded in 96-well plates at the density of 10,000 cells per well, grown at 37 °C and serum starved for 24 h. Apoptosis was induced with 1 µM staurosporine treatment for 4 h in the presence or absence of 10 µM atorvastatin and 4 nM SHP1i or equal concentrations of their respective controls (DMSO, SWNT). For quantification, an iD3 luminometer (Molecular Devices) was used.
RNA isolation and qPCR
To measure Cd47 expression, mouse smooth muscle cells and murine bone marrow-derived macrophages were exposed to DMSO, 10 µM atorvastatin, 50 ng ml−1 of TNF-α + DMSO or 50 ng ml−1 of TNF-α + 10 µM atorvastatin for 48 h. To measure ApoE, Gpx3, Rbl1, Rhob and Xiap expression, bone marrow-derived macrophages were exposed to DMSO or 10 µM atorvastatin for 48 h. RNA was extracted from cell lysates using the miRNeasy Mini Kit (QIAGEN, catalog no. 217004) according to the manufacturer’s protocol or the TRIzol method (Invitrogen, catalog no. 15596026). Then, RNA was quantified with a NanoDrop One (Thermo Fisher Scientific). RNA was reverse transcribed using the High-Capacity RNA-to-cDNA Synthesis Kit (Applied Biosystems, catalog no. 4387406). Then, qPCR of the cDNA samples was performed on a ViiA7 Real-Time PCR system or a QuantStudio 5 (both Applied Biosystems). Gene expression levels were measured using TaqMan Universal Master Mix II (Applied Biosystems, catalog no. 4440047, lot no. 00762728) and commercially available TaqMan primers (Applied Biosystems). Data were quantified using the 2−ΔΔCt method and normalized to Gapdh as an internal control. The following TaqMan Primers were used: Cd47 (Mm00495011_m1), ApoE (Mm01307193_g1), Gpx3 (Mm00492427_m1), Rbl1 (Mm01250721_m1), Rhob (Mm00455902_m1), Xiap (Mm01311594_mH) and Gapdh (Mm99999915_g1).
To measure Cd47 expression, mouse smooth muscle cells and bone marrow-derived macrophages were exposed to DMSO, 50 ng ml−1 of TNF-α + DMSO or 50 ng ml−1 of TNF-α + 10 µM atorvastatin for 48 h. Cells were washed, harvested and stained with an anti-CD47 antibody (BD Life Sciences, catalog no. 561890, FITC, MIAP301, 0.5 mg ml−1) or an isotype control antibody (BD Life Sciences, catalog no. 553929, FITC, R35-95, 0.5 mg ml−1) after Fc receptor blockade (BD Biosciences, catalog no. 553142, anti-mouse CD16/CD32). Expression was quantified using the LSRII equipped with FACSDiva v.6 software (BD Life Sciences, Stanford Shared FACS Facility) and analyzed by FlowJo 10.7.1 (BD Life Sciences). The RFI was calculated by dividing the median fluorescence intensity of CD47 by the median fluorescence intensity of the IgG isotype control.
In vitro immunofluorescence
Mouse smooth muscle cells were seeded in Millicell EZ Slides (Sigma-Aldrich, catalog no. PEZGS0416 or PEZGS0816). For CD47 staining, cells were exposed to DMSO, 50 ng ml−1 of TNF-α + DMSO or 50 ng ml−1 of TNF-α + 10 µM atorvastatin for 48 h. For NF-κB1 p105/p50 staining, cells were first treated with DMSO, 10 µM atorvastatin or 10 µM atorvastatin + 100 µM mevalonate for 24 h and then exposed to 50 ng ml−1 of TNF-α for 45 min. After stimulation/treatment, cells were rinsed with PBS and fixed with 4% phosphate-buffered paraformaldehyde. For CD47 staining (BioXCell, MIAP410, 25 µg ml−1), vector mouse-on-mouse fluorescein Immunodetection Kit (Thermo Fisher Scientific, catalog no. NC9801950) was used according to the manufacturer’s instructions. For NF-κB1 p105/p50 staining, cells were blocked with 5% goat serum (Sigma-Aldrich, catalog no. G9023) for 30 min, then incubated with NF-κB1 p105/p50 (Cell Signaling Technology, catalog no. 13586S, D4P4D, 1:200) overnight at 4 °C. After extensive washing, cells were incubated with Alexa Fluor-594 goat anti-mouse (Thermo Scientific, catalog no. A-11005, lot no. 1696463, 1:300) or Alexa Fluor-647 goat anti-rabbit (Thermo Scientific, catalog no. A-21244, lot no. 56897A, 1:300) and DAPI. Images were captured using a Leica DMi8 microscope (equipped with a Leica DMC4500 color camera and a Leica K5 camera for fluorescence imaging).
Luciferase reporter assay
The luciferase reporter assay was performed as previously described11. CD47 LightSwitch Promoter Reporter GoClones (RenSP, S710450) and Cypridina TK Control constructs (pTK-Cluc, SN0322S) were obtained from SwitchGear Genomics. The RenSP reporter, 45 ng, and the pTK-Cluc reporter construct, 5 ng, of the pTK-Cluc reporter construct were transfected into mouse smooth muscle cells using Lipofectamine 3000 Transfection Reagent (Thermo Fisher Scientific, catalog no. L3000-008) and Opti-MEM I Reduced Serum Medium (Thermo Fisher Scientific, catalog no. 31985062). After 48 h, the medium was changed to fresh medium and cells were then exposed to DMSO, 50 ng ml−1 of TNF-α + DMSO or 50 ng ml−1 of TNF-α + 10 µM atorvastatin. The cell lysate and supernatant were harvested 24 h after stimulation/treatment and dual luciferase activity was measured with the LightSwitch Luciferase Assay Kit (Active Motif, catalog no. 32031, NC0999256) and Pierce Cypridina Luciferase Glow Assay Kit (Thermo Fisher Scientific, catalog no. PI16170) using an iD3 luminometer (Molecular Devices). Relative luciferase activity (RenSP:Cypridina ratio) was quantified as the percentage change relative to the basal value obtained from control-transfected cells.
Protein extraction and western blotting
To measure NF-κB1 p50 nuclear translocation, mouse smooth muscle cells were first treated with DMSO, 10 µM atorvastatin or 10 µM atorvastatin + 100 µM mevalonate for 24 h and then exposed to 50 ng ml−1 of TNF-α for 45 min. Total protein was isolated from mouse smooth muscle cells using a subcellular protein fractionation kit (Thermo Fisher Scientific, catalog no. 78840) supplemented with Halt Protease and Phosphatase Inhibitor Cocktail (Thermo Fisher Scientific, catalog no. 78442). The protein concentration in each sample was measured using Pierce BCA Protein Assay Kit (Thermo Fisher Scientific, catalog no. 23225). Equal amounts of protein were loaded and separated on precast gels (BioRad, catalog no. 456-1084) and thereafter transferred on to polyvinylidendifluorid membranes (Life Technologies, catalog no. LC2002). After 1 h of incubation in 5% BSA in 0.1% Tris-buffered saline–Tween 20 (TBS-T), these membranes were probed with commercially available antibodies designed to recognize NF-κB1 p105/p50 (Cell Signaling Technology, catalog no. 13586 S, D4P4D, 1:1000) and HDAC1 (Cell Signaling Technology, catalog no. 5356S, 10E2, 1:1,000) overnight at 4 °C. After extensive washing with 0.1% TBS-T, membranes were incubated with secondary antibodies, Alexa Fluor-647 goat anti-mouse (Invitrogen, catalog no. 32728, lot no. TA252659, 1:10,000) and Alexa Fluor-488 goat anti-rabbit (Thermo Fisher Scientific, catalog no. A11034, lot no. 2110499, 1:10,000), for 1 h. Membranes were then scanned with an iBright 1500 Imaging System (Thermo Fisher Scientific) for quantitative analysis using Image J/FIJI software (v.2.0.0/1.52p, NIH).
Human carotid artery tissue
The Munich Vascular Biobank contains human atherosclerotic plaques and plasma samples, along with clinical data obtained from patients receiving carotid endarterectomy. The authors state that the present study complies with the Declaration of Helsinki, the locally appointed institutional review board of the Technical University Munich (Germany) has approved the research protocol and informed consent has been obtained from the subjects. In the present study, a total of 14 human carotid endarterectomy samples were used as follows: age-, gender-, medication-, symptom- and physical status-matched samples from seven patients with statin medication were compared with seven patients without such a medication (clinical data provided in Source Data Fig. 4h). In an attempt to understand why these individuals were not treated with a statin, we reviewed each subject’s clinical information. We found that those individuals not on a statin were being seen by a vascular surgeon specialist for the first time and did not have other obvious contraindications to statin use, such as liver failure or medical allergies, which could have confounded the results. Nevertheless, patients who present for carotid endarterectomy are in general candidates for statin treatment and thus the ‘No statin’ group may represent a skewed population. Of note, symptomatic stenosis was defined if the patient had suffered from carotid-related symptoms, such as transient ischemic attack, amaurosis fugax (lasting <24 h) or stroke (defined as loss of neurological function lasting >24 h), within the last 6 months. Thus, our definition refers to clinical events and not a specific degree of stenosis. Carotid tissue was cut in approximately 50-mg pieces on dry ice. Homogenization of the tissue was performed in 700 µl of QIAzol lysis reagent and total RNA was isolated using the miRNeasy Mini Kit (QIAGEN), according to the manufacturer’s instruction. RNA concentration and purity were assessed using NanoDrop (Thermo Fisher Scientific). RNA integrity numbers for all samples were assessed using the RNA Screen Tape (Agilent) in the Agilent TapeStation 4200. Next, first-strand cDNA synthesis was performed with the High-Capacity-RNA-to-cDNA Kit (Applied Biosystems), following the manufacturer’s instruction. Gene expression levels were measured using commercially available TaqMan primers (Applied Biosystems), CD47 (Hs00179953_m1) and RPLP0 (Hs00420895_gH), on a QuantStudio 3 Cycler (Applied Biosystems) using 96-well plates.
Statistical analyses were performed using GraphPad Prism 9 (GraphPad Inc.). Mean ± 95% confidence interval (CI) was used for parametric results and median ± interquartile range (IQR) for nonparametric results. Approximately normally distributed data were analyzed using a two-tailed, unpaired Student’s t-test, a two-tailed, paired Student’s t-test, a one-way analysis of variance (ANOVA) with Tukey’s or Sidak’s multiple comparisons test or a repeated-measures ANOVA with Tukey’s multiple comparisons test. For data that were not approximately normally distributed, a two-tailed Mann–Whitney U-test or a Kruskal–Wallis test with Dunn’s multiple comparisons was used. Multiple comparisons tests were reported when the overall ANOVA or Kruskal–Wallis test had a P value <0.05. All data behind the statistical analysis and all P values are provided in Source data. P values for the differentially expressed genes (Extended Data Table 1) were adjusted for multiple testing using the Benjamini–Hochberg FDR. Pathway and upstream regulator analyses (Fig. 1b, Extended Data Fig. 1c and Extended Data Table 2) were performed using IPA (QIAGEN). The overlap P values were analyzed by Fisher’s exact test, calculated by IPA.
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
The authors declare that all data supporting the findings of the present study are available within the paper, its supplementary information files or publicly available. Raw RNA-seq data are available from the National Center for Biotechnology Information under accession no. PRJNA7337400. Source data are provided with this paper.
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We thank U. Hedin and L. Matic for the input on the clinical data. We thank M. Käller for assistance with the RNA-seq. We thank the Stanford Cell Science Imaging Facility and Servier Medical Art (www.smart.servier.com) for providing components of Fig. 5. This work was supported by the Deutsche Forschungsgemeinschaft (nos. JA 2869/1-1:1 to K.-U.J. and SFB1123 to L.M.), the Deutsche Herzstiftung e.V. (no. S/09/19 to K.-U.J.), the NIH (no. R35 HL144475 to N.J.L.), the American Heart Association (no. EIA34770065 to N.J.L.) and the German Centre for Cardiovascular Research (DZHK, Junior Research group to L.M.).
I.L.W. and N.J.L. are cofounders and directors of Bitterroot Bio Incorporated, a cardiovascular company studying macrophage checkpoint inhibition. K.-U.J., Y.K., I.L.W. and N.J.L. have filed a provisional patent (US Application serial no. 63/106,794): ‘CD47 Blockade and Combination Therapies Thereof For Reduction Of Vascular Inflammation’. The remaining authors declare no competing interests.
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Extended Data Fig. 1 RNA sequencing revealed HMG-CoA reductase inhibitor as one of the top upstream regulators of SHP-1 inhibition in macrophages.
a, Flow cytometry gating strategy for cell sorting to isolate Cy5.5-positive bone marrow-derived macrophages in each group (SHP1i versus SWNT), which were then subjected to RNA sequencing. b, Rbl1, Xiap, ApoE, Rhob, and Gpx3 expression by quantitative polymerase chain reaction in bone marrow-derived macrophages upon atorvastatin treatment (n = 6 biologically independent samples per group). c, Functional pathways enriched among all differential expressed genes (false-discovery rate < 0.10) as determined by pathway analysis (p-value of overlap). d–e, Samples from vascular smooth muscle cells, bone marrow-derived macrophages, and RAW 264.7 macrophages were collected to rule out mycoplasma contamination by polymerase chain reaction and/or biochemical detection (n = 3 biologically independent samples per group). Each datapoint represents a biologically independent sample. Data and error bars present mean ± 95 % CI for parametric results. Data of b were analyzed using a two-tailed, unpaired Student’s t-test. Data of c were analyzed using a Fisher’s Exact Test, calculated by IPA.
Extended Data Fig. 2 Combined treatment of CD47-SIRPα blockade and atorvastatin showed additive effects on atherosclerotic plaque activity in vivo.
a, Quantification of atherosclerotic lesion area (n = 9 for PBS; n = 10 for statin). TVA, total vessel area. b, Quantification of necrotic core size (n = 9 for PBS; n = 10 for statin). c, Quantification of total cholesterol, HDL, LDL, and glucose in the blood (n = 9 for PBS; n = 7 for statin). d, Quantification of atherosclerotic lesion area (n = 13 for IgG; n = 13 for anti-CD47; n = 13 for anti-CD47 + statin). e, Quantification of necrotic core size (n = 13 for IgG; n = 13 for anti-CD47; n = 13 for anti-CD47 + statin). f, Quantification of total cholesterol, HDL, LDL, and glucose in the blood (n = 10 for IgG; n = 11 for anti-CD47; n = 10 for anti-CD47 + statin). g, Quantification of atherosclerotic lesion area (n = 12 for SWNT; n = 11 for SHP1i; n = 15 for SHP1i + statin). h, Quantification of necrotic core size (n = 12 for SWNT; n = 11 for SHP1i; n = 15 for SHP1i + statin). i, Quantification of total cholesterol, HDL, LDL, and glucose in the blood (n = 11 for SWNT; n = 10 for SHP1i; n = 12 for SHP1i + statin). j–l, Quantification of atherosclerotic lesion area and necrotic core size (n = 10 for Statin; n = 13 for anti-CD47; n = 13 for anti-CD47 + statin; n = 11 for SHP1i; n = 15 for SHP1i + tatin). Each datapoint represents a biologically independent animal. Data and error bars present mean ± 95% CI for parametric and median ± IQR for nonparametric results. Data of a were analyzed using a two-tailed, unpaired Student’s t-test. Data of b–c were analyzed using a two-tailed Mann–Whitney U test. Data of d,g, and j–l were analyzed using one-way ANOVA with Sidak’s multiple comparisons test. Data of e–f and h–l were analyzed using Kruskal-Wallis with Dunn’s multiple comparisons test.
Extended Data Fig. 3 Combined treatment of CD47-SIRPα blockade and atorvastatin showed additive effects on efferocytosis rate in vitro and in vivo.
a, Flow cytometry plots depicting the staining controls for the conditions. b, Apoptosis assay to quantify the rate of programmed cell death in vitro in the presence or absence of atorvastatin, SHP1i, and dual treatment after staurosporine (STS) stimulation (n = 5 biologically independent samples per group). c, Immunofluorescence images depicting cleaved caspase-3 activity (n = 9 for PBS; n = 10 for Statin; n = 11 for SHP1i; n = 15 for SHP1i + statin). White line depicts intima. Scale bar, 50 µm; scale bar inset, 10 µm. d, Immunofluorescence images depicting the ratio of free to macrophage associated cleaved caspase-3 activity (n = 9 for PBS; n = 10 for Statin; n = 11 for SHP1i; n = 15 for SHP1i + statin). White line depicts intima. *free cleaved caspase-3. #macrophage-associated cleaved caspase-3. Scale bar, 50 µm; scale bar inset, 10 µm. Each datapoint represents a biologically independent sample. Data and error bars present mean ± 95 % CI for parametric results. Data of b were analyzed using one-way ANOVA test.
Extended Data Fig. 4 Atorvastatin inhibited NF-κB1 p50 nuclear translocation under atherogenic conditions and thus directly regulated gene expression of Cd47.
a, Cd47 expression by quantitative polymerase chain reaction in bone marrow-derived macrophages (n = 6 biologically independent samples per group). TNF-α, tumor necrosis factor-α. b, Cd47 expression by flow cytometry in bone marrow-derived macrophages (n = 4 biologically independent samples per group). RFI, ratio of median fluorescence intensity. c, Cd47 expression by immunofluorescence in smooth muscle cells (n = 10 cells for vehicle and n = 15 cells for TNF-α or TNF-α + Statin examined over three biologically independent samples per group). SMC, smooth muscle cells. Scale bar, 10 µm. d, NF-κB1 p50 nuclear translocation by immunofluorescence in smooth muscle cells (n = 3 biologically independent samples per group). M, mevalonate. Scale bar and scale bar inset, 10 µm. Each datapoint represents a biologically independent sample. Data and error bars present mean ± 95 % CI for parametric and median +/− IQR for non-parametric results. Data of a were analyzed using one-way ANOVA test. Data of b were analyzed using Kruskal-Wallis test.
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Jarr, KU., Ye, J., Kojima, Y. et al. The pleiotropic benefits of statins include the ability to reduce CD47 and amplify the effect of pro-efferocytic therapies in atherosclerosis. Nat Cardiovasc Res 1, 253–262 (2022). https://doi.org/10.1038/s44161-022-00023-x
Nature Cardiovascular Research (2022)
Nature Reviews Cardiology (2022)