SUPPLEMENTARY APPENDIX
Intracoronary imaging, cholesterol efflux and transcriptomes after intensive statin treatment
YELLOW II study: Reduction in Coronary Yellow Plaque, Lipids and Vascular Inflammation by
Aggressive Lipid Lowering
Annapoorna S Kini1, Yuliya Vengrenyuk1, Khader Shameer2, Akiko Maehara3, Meerarani
Purushothaman1, Takahiro Yoshimura1, Mitsuaki Matsumura3, Melissa Aquino1, Nezam Haider1, Kipp W
Johnson2, Ben Readhead2, Brian A Kidd2, Jonathan E Feig1, Prakash Krishnan1, Joseph Sweeny1,
Mahajan, Milind1, Pedro Moreno1, Roxana Mehran1, Jason C Kovacic1, Usman Baber1, Joel T Dudley2,
Jagat Narula1, Samin Sharma1
1
Mount Sinai Hospital and Icahn School of Medicine at Mount Sinai, New York, NY, USA
2
Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New
York, NY, USA.
3
Columbia University Medical Center and Cardiovascular Research Foundation, New York, NY, USA
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Table of contents
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Intravascular image acquisition
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NIRS/IVUS image analysis
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OCT image analysis
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Assessment of cholesterol efflux capacity
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Migration assay
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Gene expression profiling
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Differential expression profiling and phenotype-specific module discovery
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Biological Interpretation of differentially expressed genes
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Table S1. Baseline characteristics
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Table S2. Clinical events
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Table S3. Grayscale IVUS qualitative evaluation
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Table S4. Grayscale IVUS volumetric analysis
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Table S5. Grayscale IVUS Planar Analysis
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Table S6. Near infrared spectroscopy parameters at baseline and follow-up
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Table S7. Baseline characteristics according to the change in FCT at the follow-up
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Table S8. Baseline characteristics according to the change in CEC at the follow-up
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Figure S1. Screening and exclusion flow diagram in YELLOW II study
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Figure S2. Effect of high-dose statin therapy on macrophage migration
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Figure S3. Correlates of change in fibrous cap thickness stratified by use of statin
at baseline.
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Figure S4. Molecular Modules driving various clinical phenotypes derived using
Weighted Gene Coexpression Network modules from PBMCs in the setting of
high-dose statin treatment
Page 30
Figure S5. Canonical pathway reconstruction using differentially expressed genes
and WGCNA modules
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Figure S6. Enriched pathways
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Figure S7. Projection of WGCNA module genes to human pathways in Reactome
using genes associated with different modules and phenotypes
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Figure S8. Visual summary of Human Phenotype Ontology terms
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Figure S9. Visual summary of PheWAS-terms
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Figure S10. Visual summary of enriched GO terms associated with differentially
expressed genes
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Figure S11. Visual summary of enriched GO terms associated with genes in
Midnight Blue module
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Figure S12. Visual summary of enriched GO terms associated with genes in
Grey60 module
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Figure S13. Visual summary of enriched GO terms associated with genes in
Lightgreen module
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Intravascular image acquisition
OCT image acquisition was performed using commercially available C7-XRTM OCT Intravascular
Imaging System with continuous intracoronary contrast injection (Visipaque; total volume 12–16 ml
injected at 3-4 ml/sec). The OCT catheter was placed at least 10 mm distally to the imaging target lesion;
each OCT pullback imaged a total of 54 mm of the vessel. Commercially available TVC Imaging System
with the TVC Insight catheter (Infraredx, Burlington, Massachusetts) was used to perform combined
IVUS and NIRS image acquisition for the same lesion. Study patients were also enrolled in the COLOR
(Chemometric Observation of Lipid Core Plaques of Interest in Native Coronary Arteries) registry
partially supported by InfraReDx, Inc. Image data were archived and sent to an independent core
laboratory (Cardiovascular Research Foundation, New York, New York) for off-line analyses.
In the IVUS image, the proximal reference was defined as the site with the largest lumen with the least
plaque burden proximal to a stenosis but within the same segment (usually within 10 mm of the stenosis
with no intervening major branches). The distal reference was defined as the site within the largest lumen
with the least plaque burden distal to a stenosis but within the same segment (also usually within 10 mm
of the stenosis with no intervening major branches). The lesion was defined as the segment from the distal
to the proximal reference. During imaging, NIRS was automatically co-registered with IVUS; therefore,
the NIRS analysis segment was defined by the IVUS segment. OCT was co-registered with IVUS using
fiduciary points (side branches, calcium deposits, etc.), and the same segment was analyzed. Baseline and
follow-up images were analyzed independently. In cases when OCT did not include the entire segment as
defined by IVUS, only the matched segment between baseline and follow-up were included.
NIRS/IVUS image analysis
The NIRS chemogram displays the distribution of the probability of lipid-rich plaque (LRP) with the Xaxis indicating the pullback position (1 pixel every 0.1 mm) and the Y-axis indicating the circumferential
position (1 pixel every 1°). Lipid core burden index (LCBI), the fraction of pixels indicating lipid within a
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region of interest, was calculated as pixels with a probability of LRP >0.6 divided by all viable pixels
multiplied by 1000. Lesion LCBI and maximum LCBI in any 4-mm (maxLCBI4mm) or 10-mm region
(maxLCBI4mm) were calculated. The block chemogram is a summary metric that is computed to display
the probability that a LRP using the top 10th percentile pixel information of the corresponding 2mm NIRS
chemogram segment. If the probability of the top 10th percentile is 0.98, the entire block is assigned
yellow; if the probability of the top 10th percentile is 0.84-0.98, the entire block is assigned tan; if the
probability of the top 10th percentile is 0.57-0.84, the entire block is assigned orange; and if the
probability of the top 10th percentile is 0.57-0.84, the entire block is assigned red. NIRS image analysis
was performed off-line using an in-house Matlab-based software programmed at the Cardiovascular
Research Foundation that read the information of each pixel and counts total viable and non-viable pixels.
IVUS images were analyzed off-line using computerized planimetry software (echoPlaque 4.0, INDEC
Medical Systems, Inc, Santa Clara, CA). Quantitative analysis included measurements every 1mm of the
external elastic membrane (EEM) and lumen cross-sectional areas (CSA). Plaque+media CSA was
calculated as EEM minus lumen CSA, and plaque burden was calculated as plaque+media divided by
EEM CSA. Once a complete set of CSA measurements was obtained, volumes (EEM, plaque+media, and
lumen) for the entire lesion were calculated using Simpson’s rule and normalized for each length. Percent
plaque+media was calculated as plaque+media divided by EEM volume. Volumetric analysis was
performed only for lesions in which motorized pullback was reliable; and planar analysis at the minimum
lumen area (MLA) site and proximal and distal most-normal-looking reference sites (defined the least
plaque burden with the largest lumen area before any side branch) was performed. Remodeling index was
calculated as the EEM CSA divided by the average of EEM CSA at proximal and distal reference.
Qualitative analysis included attenuated plaque, calcified plaque, and plaque rupture. Calcified plaque
was defined as hyperechoic plaque with acoustic shadowing. Attenuated plaque was defined plaque with
ultrasonic attenuation without calcification. Plaque rupture was defined as a cavity within a plaque that
communicated with the lumen and with an overlying fibrous cap.
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OCT image analysis
Lipid-rich plaque was defined as a diffusely bordered signal-poor region with signal attenuation and an
overlaying signal-rich layer, fibrous cap. Plaque with attenuation has fast signal drop-off, indicating
lipidic plaque or macrophages superficial to non-lipidic plaque; attenuation behind a signal-rich surface
region with a narrow trailing shadow that changes frame-by-frame was considered to represent foamy
macrophages only rather than lipidic plaque. Calcification was defined as a signal poor or heterogeneous
region with a sharply delineated border. Length was calculated using the total number of frames for each
finding, frame rate, and pullback speed. For the entire lesion, arc of lipidic plaque, macrophage, and
calcification was measured every 1 mm and then averaged (mean lipidic plaque arc = total lipidic plaque
angle/lesion length). Lipid volume index was calculated as averaged lipid arc multiplied by lipid length.
The fibrous cap thickness of each lipid-rich plaque was measured 3 times at its thinnest part and the
average value was calculated. As a sub-category of lipid-rich plaque, thin-cap fibroatheroma (TCFA) was
defined as lipid-rich plaque with minimal fibrous cap thickness less than 65 µm. Micro-vessels within the
intima appear as signal-poor voids that are sharply delineated and can usually be followed in multiple
contiguous frames. Cholesterol crystal was defined as a thin, linear region of high intensity. A calcified
nodule is calcium that protrudes into the lumen, frequently forming sharp, jutting angles. Thrombus was
defined as a mass attached to luminal surface or floating within the lumen. If the thrombus is signal-rich
with high attenuation, it is considered a red-cell rich thrombus; and if the thrombus is homogeneous
signal-poor without attenuation, it is considered a platelet-rich thrombus. Ruptured plaques have intimal
tearing, disruption, or dissection of the cap with a cavity
Assessment of cholesterol efflux capacity
Cholesterol efflux capacity (CEC) was measured at baseline and follow-up as previously described (1,2).
Patient serum was separated within 2 hours of collecting blood samples and aliquots were stored at -800C.
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J774 cells, derived from mouse macrophage cell line, were plated and radiolabeled with 2 μCi of 3Hcholesterol (Perkin Elmer, Waltham, USA) per ml of medium. ABCA1 was then upregulated by
incubating the cells with 0.3 mmol of L8-(4-chlorophenylthio)-cyclic AMP (Sigma-Aldrich, St Louis,
USA), since J774 cells have low levels of ABCA1 activity. Subsequently, efflux medium containing
2.8% ApoB - depleted serum was added to the cells and incubated for 4 h to measure efflux. To prepare
ApoB-depleted serum, the samples were thawed and 40 parts of polyethylene glycol solution (20%
polyethylene glycol 8000 molecular weight in 200 mmol/L glycine buffer, pH 7.4, Fisher Scientific,
Pittsburg, USA) were added to 100 parts of serum; samples were mixed by pipetting and incubated at
room temperature for 20 min to precipitate ApoB. The mixture was then centrifuged at 10,000 rpm for 30
min at 40C. ApoB-containing lipoproteins were pelleted by this procedure and the supernatant, which
contained the HDL fraction, was recovered. The ApoB-depleted serum was diluted in 14 mmol/L MEMHEPES buffer (no bicarbonate; Fisher Scientific, Pittsburg, USA) containing 0.15 mmol/L cAMP
(Sigma-Aldrich, St Louis, USA) to 1.4% (equivalent to 1% serum). Liquid scintillation counting was used
to quantify the efflux of radioactive cholesterol from J774 cells. The quantity of radioactive cholesterol
incorporated into cellular lipids was measured by means of isopropanol extraction of control wells not
exposed to patient serum. The percentage of efflux was calculated by the following formula: (μCi of 3Hcholesterol in mediums containing 2.8% apo B depleted serum – μCi of 3H-cholesterol in serum-free
mediums) / (μCi of 3H-cholesterol in cells) × 100. All the assays were performed in triplicate. To correct
for interassay variation across plates, pooled serum control from eighteen healthy volunteers was included
on each plate, and normalized the values for serum samples from patients to this pooled value in
subsequent analysis.
Migration assay
Migration of J774 macrophages toward CCL19 (100 ng/ml) and CCL21 (100 ng/ml) was measured using
24-well plates and Transwell inserts with 5 µm pore (Corning) as previously described (3,4). ApoBdepleted serum was prepared from baseline and follow-up samples of each patient as described in the
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“Assessment of cholesterol efflux capacity” section above. J774 cells (ATCC® TIB-67™, VA, USA)
were maintained in RPMI-1640 medium supplemented with 0.25 mg/ml of Gentamycin and 10% FBS, at
37 °C under humidified 5% CO2–95% air. For activation into foam cells, freshly cultured J774 at 5x105
cells/ml were treated with 50 μg/ml of acetylated low-density lipoprotein (Ac-LDL) (Alfa Aesar, MA,
USA) for 24 hours at 37 °C, 5% CO2–95% air. Prior to migration assay, cells were washed twice with
and re-suspended in serum free RPMI-1640 medium (SFM). Ac-LDL activated cells (3x105 cells in 300
μg SFM) were treated with 5 μg of ApoB - depleted serum for 8 hours at 37 °C, 5% CO2–95% air. For
each patient, baseline and follow-up samples were analyzed in pair. For each sample, Ac-LDL and
ApoB-depleted serum treated cells (1.0x105 cells in 100 μl SFM) were added to the upper chamber. Filter
inserts were placed in the wells containing 650 μl of chemotaxis medium, 100 ng/ml of CCL19 and 100
ng/ml of CCL21 (R&D Systems, MN, USA) in SFM. Cells were allowed to migrate for 16 hours at 37
°C, 5% CO2–95% air. Assay for each sample was performed in triplicate. After removing the medium
and rinsing the Transwell inserts with DPBS, the non-migrated cells on the upper surface of the filters
were removed by gently wiping with a cotton swab. Cells that had migrated to the lower face of each
filter were stained with 1% crystal violet stain aqueous solution for 10 minutes. Inserts were rinsed with
water to remove excess stain and allowed to air dry completely. Stained cells were visualized under the
bright field of Zeiss Axiovert CFL M inverted microscope at 100X magnification. Imaging software NISElements F2.30 (Nikon) was used for image acquisition and Image J 1.46r (NIH) for cell counting. The
number of stained cells of three random fields within each Transwell insert was counted, and for each
sample, cell count is an average of triplicate Transwell insert. For each patient, result was expressed as a
ratio of cell count in follow-up to the corresponding baseline cell number.
Gene expression profiling
Peripheral blood mononuclear cells were isolated by density gradient centrifugation using Ficoll-paque
premium (GE Healthcare, Marlborough, MA). In brief, buffy coat was isolated from 20 ml of blood and
mixed with 20 times the volume of DPBS and layered over 10 ml of Ficoll-paque and centrifuged. The
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PBMC layer was removed, washed and centrifuged twice with DPBS. The cells were counted using a
hemocytometer and processed for RNA isolation using PureLink RNA mini kit with TRIzol reagent from
Ambion (Life technologies, Carlsbad, CA) per the manufacturer’s instructions. The kit includes PureLink
DNase treatment to remove DNA from the sample allowing subsequent purification of RNA. The RNA
concentration and quality was assessed on an Agilent BioAnalyzer 2100 RNA Nano 6000 Series II
according to manufacturer’s instructions and stored at −80°C. Gene expression profiling was performed
using Illumina Human HT-12 v4 bead chip array. Specific gene expression signals were validated with
Taqman primers and probes listed below using 7500 fast DX real time PCR instrument (Applied
Biosystems, Carlsbad, CA). Gene expression data was submitted to the Gene Expression Omnibus (GEO)
database with the series accession ID GSE86216.
Gene Symbol
Applied Biosystems Catalog no
Gene Symbol: 18S
Assay ID: Hs99999901_s1
Gene Symbol: DHCR24, hCG33029
Assay ID: Hs00207388_m1
Gene Symbol: SQLE, hCG14874
Assay ID: Hs01123768_m1
Gene Symbol: FADS1, hCG40849
Assay ID: Hs00203685_m1
Gene Symbol: LDLR, hCG29965
Assay ID: Hs01092524_m1
Gene Symbol: ABCA1, hCG1789838
Assay ID: Hs01059118_m1
Gene Symbol: ABCG1, hCG401262
Assay ID: Hs00245154_m1
Differential expression profiling and phenotype-specific module discovery
Differential expression signatures were generated using the Bioconductor-based lumi and limma
packages (5-7). A variance-stabilizing transformation was applied to background-adjusted probe
intensities, and normalization was performed with the quantile method (8). Gene filtering was conducted
with the genefilter Bioconductor package
(http://www.bioconductor.org/packages//2.11/bioc/html/genefilter.html) to remove low-variance and non9
expressed probes prior to analysis. Correction for batch effects resulting from microarray processing was
performed with the ComBat function in the R package SVA using a parametric empirical Bayes technique
(7,9). Probe-to-gene mapping was conducted using the nuID mapping scheme implemented in lumi (10).
Differential expression levels of genes perturbed due to the high-dose statin treatment were estimated
with the empirical Bayes method to estimate linear regression coefficients. Linear regression covariates
accounted for the paired design of the study. Resulting expression P-values were corrected with the
Benjamini-Hochberg method at a false discovery rate (FDR) of 5%. Weighted gene coexpression network
analysis (WGCNA) was used to discover gene modules significantly correlating to clinical phenotypes
(11,12). Briefly, WGCNA uses pairwise probe-level expression correlations from microarray data to
construct co-expressed modules of genes using hierarchical clustering. The eigengenes of these modules
(the first principal component of the expression correlation matrices) can be tested for association with
clinical phenotypes, and the biological function of each module explored by testing for enrichment for
various gene ontologies. Gene lists and modules were analyzed using Enrichr (13) to query multiple gene
function databases, canonical pathways were compiled using Ingenuity Pathway Analysis (QIAGEN,
Hilden Germany), and enriched pathways were mapped to a global human pathway map using Reactome
(14).
Biological Interpretation of differentially expressed genes
A total of 117 differentially expressed genes with 39 downregulated genes and 78-upregulated genes were
identified using differential expression analyses. The magnitude of expression and differentially
expressed genes were comparable to previous studies that profile the transcriptomic impact of statin
therapy (15-18). From WGCNA analysis (Figure S4), we discovered one module associated with CEC
and two modules associated with FCT. The four gene lists from the study were analyzed using three
different analytical approaches: GO term enrichment analyses, pathway and network enrichment analyses,
and disease and phenomic enrichment analyses. GO terms, pathways, networks and disease phenotypes
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significant after multiple-testing correction using Benjamini Hochberg (P<=0.05) are compiled for
interpretation. Only representational examples are discussed here; detailed supplemental data is provided
in Gene Ontology Term Enrichment Analyses (page 13). In addition to the three categories we have
augmented our enrichment analyses by including data from ENCODE ChEA (19), TRANSFAC (20) and
JASPAR (21) databases to understand transcription-factor based regulation of genes. Data from the
CORUM database (22,23) is integrated to understand protein-complexes enriched across the gene lists.
Finally, protein-domain annotation data from Pfam (24) and InterPro (25) databases were integrated to
infer conserved protein domains enriched across the gene-lists. Biocuration with literature review and
functional enrichment analyses was used to reveal the molecular context of the differentially expressed
genes in cardiovascular diseases and molecular mechanisms driving the phenomic changes leading to the
outcome improvement in CEC and FCT. Similar approaches have been used for biological interpretation
of gene lists from PBMC-based microarray and WGCNA modules (26-29). Targeted, deep resequencing
studies of the significantly enriched genes in large cohort and biochemical studies using model organisms
targeting pathways and mechanisms related to could reveal the precise role of interferon molecules in
mediating the serial changes in statin response phenotypes.
Gene ontology (GO) (30) enrichment analyses provide a global overview of the function, cellular context
and biological mechanisms of the gene lists. GO term enrichment analyses were performed using Enrichr
and diacyclic graph structure with enriched terms were visualized using WebGestalt (31). A biochemical
pathway represents an interconnected set of biochemical activities that start with a set of the first
molecule and an end product (for example the glycolysis pathway or statin metabolism). The biological
networks represent a set of connected genes or gene products where the connections between the nodes
are derived from various biological features (protein-protein interaction, drug-target interaction,
correlation of coexpression between two genes). Pathway analyses help to identify overrepresented
pathways in the genes and network analyses helps to understand functional systems that are highly
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connected across the gene lists compared to a background of the human reference genome. Pathway and
network enrichment analyses were performed using Reactome annotations via Enrichr and visualized
using Reactome global pathway map. Canonical pathways were identified and visualized using IPA.
Disease associations of the genes across different gene lists were compiled using data from dbGAP (32),
PheWAS and the IPA database. In contrast to GWAS, which is used to identify genetic variants,
Phenome-wide enrichment analyses, phenome-wide enrichment analyses (PheWAS) test for phenotypic
association with genetic variants and thus provides a cohesive view of the genotype-phenotype map
driving diseases via pleiotropic hubs. Phenomic enrichment analyses using Human Phenotype Ontology
(HPO: Figure S8) and over-representation using PheWAS data (Figure S9) was performed using
WebGestalt. GWAS-PheWAS studies have recently elucidated an association between several genetic
variants and various complex diseases including several cardiovascular pathologies (myocardial
infarction, peripheral arterial disease, type-2 diabetes etc.) (33-37). PheWAS results could be relevant for
discovery of novel genes related to change in plaque morphology, but further studies are required to
delineate the precise role of these genes’ phenomic impact to the cardiovascular system. Further studies
are required to delineate the precise role of these genes’ phenomic impact to cardiovascular system.
Gene Ontology Term Enrichment Analyses
Differentially expressed genes: Significantly enriched GO terms are listed in Supplementary File 3
Enrichment Analyses; also see Figure S10. 23 biological processes terms were significant, including
terms comprising response to interferon-gamma (GO:0034341): IFITM3; SYNCRIP; MT2A; GCH1;
STAT1; FCGR1A; GBP2; FCGR1B; GBP1; CXCL1), cholesterol metabolic process (GO:0008203) and
sterol metabolic process (GO:0016125): ABCA1; SQLE; MBTPS1; HMGCS1; INSIG1; CYP51A1;
DHCR24; LDLR; ABCG1). Cellular component level enrichment analyses revealed two terms
significantly associated with endosomal localization of several corresponding genes; endosomal part
(GO:0044440) and endosome membrane (GO:0010008): TICAM2; ZFYVE20; FCGR1A; FCGR1B;
LDLR; MYD88; RHOB (38).
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Midnight Blue (CEC): Significantly enriched GO terms are listed in Supplementary File 3 Enrichment
Analyses; also see Figure S11. 84 biological process terms were significantly enriched representing broad
functional categories of immune and inflammatory responses, and fat cell differentiation. Genes were also
enriched for multiple signal transduction events (T cell receptor signaling pathway (GO:0050856: TRAT1;
CCR7; UBASH3A), antigen receptor-mediated signaling pathway (GO:0050854: TRAT1; CCR7;
UBASH3A), apoptotic signaling pathway (GO:0097190: BAG3; BNIP3; CD28; CD27; PRKCA; FHIT)
neurotrophin signaling pathway (GO:0038179), fibroblast growth factor receptor signaling pathway
(GO:0008543: TRAT1; CD28; PRKCA; NFKB1; FOXO1) stress-activated protein kinase signaling
cascade (GO:0070302: MYC;CD27;CCR7;FOXO1), cAMP-mediated signaling (GO:0043951: CRTC3;
PDE3B) and I-kappaB kinase/NF-kappaB signaling (GO:0043123: EDAR; IL23A; CD27; CCR7)).
Grey60 (FCT): Significantly enriched GO terms are listed in Supplementary File 3 Enrichment
Analyses; also see Figure S12. 73 biological process terms, 11 cellular component terms and 7 molecular
function terms were significantly enriched representing the broad functional categories of immune and
inflammatory response, collagen catabolic process, phagocytosis, and extracellular matrix organization. A
subset of 15 genes were driving immune and inflammatory response-related processes (CEBPE; ANXA3;
DEFA4; HP; DEFA3; RNASE3; DEFA1; AZU1; MPO; BPI; CTSG; PGLYRP1; CAMP; ELANE; LTF).
Collagen catabolism and extracellular matrix reorganization was mediated by six genes (COL17A1;
ARG1; PRTN3; MMP8; MMP9; ELANE). Eight genes (CEBPE; ANXA3; ELANE; PRTN3; AZU1;
OLFM4; CAMP; LTF) mediating phagocytosis and related cellular mechanisms were enriched in the
module. Cellular component level enrichment suggests localization of genes enriched across extracellular
regions and localized to endocytic process related cellular components. Molecular functions including
peptidase activity mediated by seven genes (HP; PRTN3; CTSG; AZU1; MMP8; ELANE; LTF) and
heparin binding activity mediated by (CTSG; AZU1; MPO; ELANE; LTF) were significantly associated
with the module.
13
Lightgreen (FCT): Significantly enriched GO terms are listed in Supplementary File 3 Enrichment
Analyses; also see Figure S13. Significant transcription factor mediated regulation was observed in this
module For example: data from ENCODE suggests that 10 genes (CRBN; YTHDC1; PAPD5; SPATA13;
DBT; ZBTB43; PTPLAD1; RORA; THOC2; RPAP2) are regulated by HCFC1. While some of these
genes are associated with cardiovascular clinical traits, understanding the specific role of these genes in
cardiovascular pathology and FCT dynamics would need additional studies (39-41).
Pathways and network analyses:
Differentially expressed genes: 11 canonical networks (Supplementary File 3 Enrichment Analyses) and
seven Reactome pathways were enriched. Pathways like Regulation of cholesterol biosynthesis by
SREBP (SREBF) (MBTPS1; SQLE; HMGCS1; SCD; INSIG1; CYP51A1) and Activation of gene
expression by SREBF (SQLE; HMGCS1; SCD; CYP51A1) suggest a activation of Sterol regulatory
element-binding proteins (SREBP) and related pathways. SREBP plays a major role in the homeostasis of
cholesterol and lipid molecules (42). Subsets of differentially expressed genes also have role in
cholesterol pathways like Cholesterol biosynthesis (SQLE; HMGCS1; CYP51A1; DHCR24). Genes
involved in signal transduction pathways including interferon signaling events (MT2A; STAT1; GBP2;
FCGR1A; GBP1; FCGR1B; GBP4), KIT signaling (LYN; SRC; SH2B3) and cytokine signaling (LYN;
IFITM3; STAT1; SRC; ARRB1; MT2A; SPRED1; IL23A; PSME2; GBP2; FCGR1A; GBP1; FCGR1B;
GBP4; MYD88)(43). The role of interferon signalling in the context of the high-dose statin treatment,
which overlaps with the 9p21 locus, suggests that targeting interferon pathways may lead to better
cardiovascular therapies (44,45). For example, signal transducer and activator of transcription 1 (STAT1)
is a transcription factor involved in selective upregulation of several interferons. This gene is upregulated
in the follow-up thus indicating the activation of interferon pathways. The differentially expressed gene
signature is also associated with six genes involved in LXR activation (ABCA1; ABCG1; CYP51A1;
LDLR; MYLIP; ORM1; SCD). LXR activation and associated molecular pathways are important for
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cholesterol homeostasis and are implicated in multiple cardiovascular structure, function and disease
phenotypes (46-49).
Midnight Blue (CEC): Nine canonical networks and nine Reactome pathways were enriched. Significant
pathways were representing signaling events mediated by five genes (TRAT1; CD28; PRKCA; FOXO1;
NFKB1). Pathways and processes mediated by genes in this module represent cross talk of multiple
pathways to mediate cholesterol efflux capacity (50-55).
Grey60 (FCT): Five canonical networks and 10 Reactome pathways were enriched. Significant pathways
representing modulation of genes involved in Extracellular matrix and Fibronectin organization and
degradation (COL17A1; CEACAM1; CEACAM6; CTSG; CEACAM8; MMP8; MMP9; ELANE),
Activation of Matrix Metalloproteinases (CTSG; MMP8; MMP9; ELANE), defensins (CAMP; LTF), Cell
surface interactions at the vascular wall (CEACAM1; CEACAM6; OLR1; CEACAM8) and defensins
(DEFA4; DEFA3; DEFA1). ECM remodeling has been identified as a hallmark feature of cardiovascular
disease, and ascertaining its specific role in the setting of CEC could further illuminate the role of ECMs
as a therapeutic target (56,57).
Lightgreen (FCT): Seven canonical networks were identified from IPA modules. Pathways including
EGFR signaling (SH2KBP1; RASA2), chromatin modifying enzymes (SUZ12; ARID4B), semaphorin
interactions mediated by phosphotyrosine phosphatases (PTPRC) were indicated, but didn’t reach
significance after multiple testing correction. Genes in this module like RORA and FAM107B were
associated with lipids and inflammation in recent GWAS studies (58).
Diseases and clinical phenotype associations:
Differentially expressed genes: Genes differentially expressed in the follow up compared to baseline
show enrichment for various cardiovascular diseases. IPA analysis suggested that differentially expressed
genes were associated with various cardiovascular disease including cardiac arteriopathy (ABCA1,
ABCG1, LDLR, CORO2A and SMAD3), congenital heart block (FGCR1A and FGCR1B), heart failure
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(NEBL) and myocardial infarction (FGCR1A, FGCR1B and SH2B3) (36,59,60). Several pleiotropic genes
(SH2B3; ABCA1; MEFV; LDLR) associated with multiple cardiovascular disease phenotypes including
Coronary artery disease (HP:0001677), Abnormality of the coronary arteries (HP:0006704),
Arteriosclerosis (HP:0002634), Atherosclerosis (HP:0002621), Abnormalities of the peripheral arteries
(HP:0005114) and Arterial stenosis (HP:0100545) were differentially expressed.
Midnight Blue (CEC): Genes in the Midnight blue module have various cardiovascular disease
implications. IPA analysis suggested tgat the genes in the module have implications in the following
cardiovascular diseases: heart failure (DPP4; PDE3B), cardiac arteriopathy (PLEKHB1; PDE3B; DPP4;
DOCK9), cardiac arrhythmia (BAG3) and cardiac infarction (LTA). We noted that PheWAS associations
suggest an interesting link between infection (SOX8; CRTC3), ADHD (MYC; CRTC3) and sinusitis
(UBASH3A; NFKB1) – suggesting the genetic variants driving inflammation as common theme (61-64).
Grey60 (FCT): Genes in the Grey60 module associated with FCT have various cardiovascular disease
implications. For example: coronary artery disease (OLFM4), eosinophilic myocarditis (RNASE3),
cardiomegaly arrhythmia (MMP9; MPO; SLC2A5), long-QT syndrome (SLC2A5), non-rheumatic aortic
stenosis (MMP9) myocardial infarction (OLR1) and lipid levels (HP and DEFA3). There was no PheWAS
association with the genes in this list. HPO enrichment suggests that this module has leukemia
(HP:0001909) and neutrophil abnormality (HP:0001874) related genes (CEBPE and ELANE).
Lightgreen (FCT): Genes in the Lightgreen module associated with FCT are implicated in some
cardiovascular disease phenotypes. For example: stroke (SPATA13; PHACTR2) coronary artery disease
(FAM107B) and atrioventricular canal defect (CRBN). HPO enrichment analyses suggests that three genes
in this module have an autosomal recessive mode of inheritance (PTPRC; DBT; CRBN), abnormality of
the skin (PTPRC and CYCLD) and cognitive impairment (DBT and CRBN) – however these genesphenotype associations were not significant after multiple testing corrections.
16
Table S1. Baseline characteristics*
Baseline (n=85)
Age, years
62.4 ± 11.2
Male gender
58 (68)
Hypertension
76 (89)
Hypercholesterolemia
75 (88)
Diabetes mellitus
37 (44)
BMI
29.6 ± 5.2
Current smoking
12 (14)
History of smoking
26 (31)
Prior MI
12 (14)
Prior PCI
24 (28)
Statin use and dose
69 (81)
atorvastatin
29 (34)
10/20/40/80 mg
4/9/7/8
simvastatin
23 (27)
10/20/40 mg
3/14/5
rosuvastatin
12 (14)
5/10/20/40 mg
1/3/1/7
pravastatin
4 (5)
20/80 mg
2/2
fluvastatin
1 (1)
20 mg
1
LAD
36 (42)
LCX
23 (27)
RCA
26 (31)
Coronary vessel
Values are mean ± SD, n (%) or n for statin dose data
*
BMI denotes body mass index, MI myocardial infarction, PCI percutaneous coronary intervention, LAD the left
anterior descending artery, LCX left circumflex artery, RCA the right coronary artery.
17
Table S2. Clinical events
Events (n = 85)
Death from cardiovascular causes
0 (0.0)
Myocardial infarction
3 (3.5)
Urgent revascularization
13 (15.3)
Nonfatal stroke
0 (0.0)
Any bleeding
0 (0.0)
Periprocedural complication
2 (2.4)
Statin discontinuation
0 (0.0)
Statin reduction
6 (7.1)
Hospitalization for chest pain
15 (17.6)
Values are n (%)
18
Table S3. Grayscale IVUS qualitative evaluation
Baseline
(n = 85)
Follow-up
(n = 85)
P value
65 (76.5)
65 (76.5)
1.00
Superficial
46 (54.1)
47 (55.3)
0.32
Deep
14 (16.5)
12 (14.1)
0.16
Mixed
5 (5.9)
6 (7.1)
0.32
4.62 ± 4.64
4.65 ± 4.44
0.81
82 (96.5)
83 (97.6)
0.99
79 (92.9)
79 (92.9)
1.00
Deep
2 (2.4)
2 (2.4)
1.00
Mixed
1 (1.2)
2 (2.4)
0.32
12.4 ± 9.06
13.15 ± 9.12
0.02
Number of lesion with attenuated plaque
Attenuated plaque location
Total length of attenuated plaque (mm)
Number of lesions with calcification
Calcium location
Superficial
Total calcium length, mm
Values are mean ± SD or n (%)
19
Table S4. Grayscale IVUS volumetric analysis*
Baseline
(n = 85)
Follow-up
(n = 85)
P value
Lesion Length by IVUS, mm
26.68 ± 9.99
26.71 ± 10.24
0.79
EEM Volume, mm3
298.2 ± 147.3
297.4 ± 149.8
0.73
Lumen Volume, mm3
115.85 ± 60.05
114.73 ± 60.16
0.26
% Plaque + Media Volume, %
60.71 ± 7.52
60.97 ± 7.57
0.30
Mean EEM CSA, mm3/mm
11.18 ± 3.43
11.15 ± 3.46
0.56
Mean Lumen CSA, mm3/mm
4.32 ± 1.37
4.28 ± 1.36
0.23
Mean Plaque + Media CSA,
mm3/mm
6.86 ± 2.46
6.87 ± 2.44
0.80
Values are mean ± SD
*
IVUS denotes intravascular ultrasound, EEM external elastic membrane, CSA cross sectional area.
20
Table S5. Grayscale IVUS Planar Analysis*
Baseline
(n = 85)
Follow-up
(n = 85)
P value
27.34 ± 13.14
27.55 ± 13.03
0.76
9.88 ± 3.60
9.98 ± 3.72
0.42
Lumen CSA, mm
2.20 ± 0.58
2.23 ± 0.60
0.45
Minimum Lumen Diameter, mm
1.55 ± 0.19
1.56 ± 0.21
0.42
Plaque + Media CSA, mm
7.67 ± 3.32
7.75 ± 3.44
0.52
Plaque Burden, %
75.93 ± 7.07
75.79 ± 7.96
0.78
Remodeling Index
0.90 ± 0.42
0.88 ± 0.32
0.33
12.07 ± 3.57
12.29 ± 3.56
0.40
10.44 ± 3.11
10.49 ± 2.95
0.22
4.99 ± 2.40
5.07 ± 2.46
0.91
38.75 ± 10.54
38.93 ± 10.57
0.86
Minimum Lumen Area Site
Length from Ostium, mm
2
EEM CSA, mm
2
2
Reference Site
2
EEM CSA, mm
2
Lumen CSA, mm
2
Plaque + Media CSA, mm
Plaque Burden, %
Values are mean ± SD
*
EEM denotes external elastic lamina, CSA cross sectional area.
21
Table S6. Near infrared spectroscopy parameters at baseline and follow-up*
Baseline
(n = 85)
Follow-up
(n = 85)
P value
142.84
(88.74, 215.80)
141.93
(96.92, 239.21)
0.69
Yellow
3.30 ± 2.91
3.45 ± 2.92
0.58
Tan
1.90 ± 1.39
1.94 ± 1.75
0.90
Orange
1.76 ± 1.52
1.64 ± 1.43
0.57
Red
6.36 ± 3.95
6.12 ± 4.35
0.91
283.19
(162.56, 357.43)
273.32
(157.89, 386.83)
0.80
403.20
(283.30, 511.96)
392.00
(276.15, 518.38)
0.43
Lesion
LCBI
Block Chemograms
Maximum 10 mm Segment
LCBI
Maximum 4 mm Segment
LCBI
Values are mean ± SD or median (25th, 75th percentile)
*
LCBI denotes lipid core burden index
22
Table S7. Baseline characteristics according to the change in FCT at the follow-up*
Decreased FCT
(n=13)
No change in FCT
(n=18)
Increased FCT
(n=39)
P-value
62.5 ± 7.9
65.2 ± 10.9
60.1 ± 12.0
0.32
Male gender
85
72
64
0.38
Hypertension
85
83
92
0.55
Hypercholesterolemia
85
94
85
0.57
Diabetes mellitus
41
67
44
0.78
29.7 ± 4.6
30.2 ± 6.3
28.0 ± 5.1
0.26
Current smoking
8
28
13
0.25
Prior MI
23
6
10
0.31
Prior PCI
15
33
26
0.54
Statin use
77
67
79
0.58
Total cholesterol, mg/dl
139.5 ± 24.8
163.1 ± 50.1
157.8 ± 50.0
0.35
LDL cholesterol, mg/dl
76.0 ± 17.9
97.0 ± 51.2
88.2 ± 41.7
0.39
HDL cholesterol, mg/dl
36.2 ± 5.3
42.3 ± 13.8
42.9 ± 14.1
0.26
Triglyceride, mg/dl
139.5 ± 24.8
163.1 ± 50.1
157.8 ± 49.7
0.86
ApoB, mg/dl
72.9 ± 12.8
87.8 ± 37.9
80.3 ± 28.7
0.40
Apo-AI, mg/dl
114.5 ± 10.5
120.9 ± 26.5
121.6 ± 27.6
0.69
hs-CRP, mg/l
2.4 ± 1.5
3.1 ± 2.9
4.3 ± 7.6
0.58
Age, years
BMI
Values are mean ± SD or %
*
BMI denotes body mass index, MI myocardial infarction, PCI percutaneous coronary intervention
23
Table S8. Baseline characteristics according to the change in CEC at the follow-up*
Decreased CEC
(n=32)
No change in CEC
(n=3)
Increased CEC
(n=50)
P-value
62.5 ± 11.1
66.7 ± 18.6
60.2 ± 10.6
0.11
Male gender
72
67
66
0.86
Hypertension
94
100
86
0.46
Hypercholesterolemia
94
67
86
0.29
Diabetes mellitus
41
67
44
0.69
29.6 ± 4.8
31.0 ± 2.2
29.4 ± 5.5
0.86
Current smoking
9
0
18
0.44
Prior MI
6
0
20
0.17
Prior PCI
31
0
28
0.53
Statin use
32
3
50
0.94
Total cholesterol, mg/dl
152.9 ± 43.3
145.7 ± 25.7
154.4 ± 47.5
0.94
LDL cholesterol, mg/dl
84.3 ± 38.0
69.9± 10.4
89.4 ± 41.8
0.65
HDL cholesterol, mg/dl
43.3 ± 15.6
31.0 ± 9.2
40.5 ± 10.6
0.23
Triglyceride, mg/dl
125.0 ± 68.2
223.7 ± 132.3
125.5 ± 131.3
0.33
ApoB, mg/dl
79.8 ± 22.9
80.3 ± 8.0
79.3 ± 32.1
0.99
Apo-AI, mg/dl
126.8 ± 28.3
106.7 ± 21.2
116.3 ± 23.1
0.13
hs-CRP, mg/l
4.1 ± 6.4
12.2 ± 16.7
2.5 ± 2.8
0.01
Age, years
BMI
Values are mean ± SD or %
*
BMI denotes body mass index, MI myocardial infarction, PCI percutaneous coronary intervention
24
Figure S1. Screening and exclusion flow diagram in YELLOW II study
Total number of patients screened: N = 962
Generally/clinically excluded: N = 31
Renal insufficiency, participating in another study
Angiographically excluded: N = 834
Normal coronaries, non-obstructive or 1 vessel CAD, ISR,
CTO, vein graft
Patients excluded based on NIRS: N = 6
Study lesion maxLCBI4mm <150
Lost to follow-up: N = 6
Final study population: N = 85
25
Figure S2. Effect of high-dose statin therapy on macrophage migration. Migration of J774 cells incubated
with acetylated LDL and pretreated with ApoB-depleted patient serum toward CCL19 (100 ng/ml) and
CCL21 (100 ng/ml) at the baseline (BL) and follow-up (FU).
26
Figure S3. Correlates of change in fibrous cap thickness stratified by use of statin at baseline. Association
between the change in fibrous cap thickness, cholesterol efflux (A, B) and migration (C, D) for statin
naïve patients (blue filled circles) and patients with statin use at baseline (green open circles) (A).
Multivariate regression models include interaction terms, Change in cholesterol efflux capacity*Baseline
statin (B) and Change in migration*Baseline statin.
A)
27
B)
Beta coefficient (95% CI)
P value
Change in cholesterol efflux capacity
0.75 (0.30 to 1.21)
0.001
Change in LDL cholesterol
0.17 (-0.01to 0.43)
0.17
Change in HDL cholesterol
0.01 (-0.22 to 0.25)
0.91
Change in hs-CRP
-0.26 (-0.45 to -0.04 )
0.02
Change in triglyceride
-0.10 (-0.30 to 0.12)
0.40
Age
-0.02 (-0.26 to 0.22)
0.87
Gender (Female)
0.15 (-0.09 to 0.40)
0.20
-0.27 (-0.53 to -0.06)
0.02
Baseline statin
0.06(-0.30 to 0.18)
0.64
Change in cholesterol efflux capacity *
Baseline statin
-0.53(-1.1 to -0.09)
0.02
Baseline fibrous cap thickness
28
C)
D)
Beta coefficient (95% CI)
P value
Change in cholesterol efflux capacity
0.34 (0.11 to 0.59)
0.006
Change in LDL cholesterol
0.05 (-0.19 to 0.30)
0.67
Change in HDL cholesterol
-0.08 (-0.31 to 0.16)
0.53
Change in hs-CRP
-0.28 (-0.46 to -0.06 )
0.01
Change in triglyceride
-0.13 (-0.33 to 0.09)
0.25
Age
0.01 (-0.24 to 0.25)
0.96
Gender (Female)
0.13 (-0.10 to 0.37)
0.27
-0.27 (-0.53 to -0.05)
0.02
Baseline statin
0.08(-0.16 to 0.32)
0.49
Change in migration
0.47(-0.11 to 1.02)
0.11
-0.68(-1.20 to -0.11)
0.02
Baseline fibrous cap thickness
Change in migration * Baseline statin
29
Figure S4. Molecular Modules driving various clinical phenotypes derived using Weighted Gene
Coexpression Network modules from PBMCs in the setting of high-dose statin treatment A) Cluster
dendrogram B) network properties and C) Modules and phenotype-correlates of modules identified using
WGCNA method representing significant modules correlated with Efflux (midnight blue;
correlation=0.208; P=0.01234) and FCT (grey60: correlation=0.164; P=0.04947 and Lightgreen:
correlation=0.164; P=0.0497). Each matrix is represented as upregulated (red), neutral (white) or
downregulated (blue) with correlation coefficient and corresponding P value to indicate statistical
significance. LArc denotes lipid arc, LLg lipid length.
30
Figure S5. Canonical pathway reconstruction using differentially expressed genes and WGCNA modules.
(A) Canonical network of 33/117 genes constructed to IPA representing inflammatory response, lipid
metabolism and small molecule biochemistry (B) FCT associated module (Grey60): Canonical network
reconstruction using 20/40 genes representing cell death and survival, cell-to-cell signaling and
interaction, hematological system development and function (C) FCT associated module (Light Green):
Canonical network reconstruction using 11/37 genes representing cancer, cell cycle, organismal injury
and abnormalities.
A)
31
B)
32
C)
33
Figure S6 Enriched pathways mediated by (A) Differentially expressed genes in follow up compared
baseline expression; (B) Efflux associated module (Midnight Blue) and (C) FCT associated module
(Grey60).
A)
34
B)
C)
35
Figure S7. Projection of WGCNA module genes to human pathways in Reactome using genes associated
with different modules and phenotypes. (a) CEC (Midnight Blue; 65 genes)) (b) FCT (Grey60; 40 genes)
(c) FCT (Light Green; 37 genes). Each pathway is represented using an arc. Dark yellow to light yellow
indicate significance levels.
36
Figure S8. Visual summary of Human Phenotype Ontology terms enriched across a) differentially
expressed gene-signature b) Midnight blue module c) Grey60 module and d) Lightgreen module.
c)
a)
b)
d)e)
37
Figure S9. Visual summary of PheWAS-terms enriched across a) differentially expressed gene-signature
b) Midnight blue module c) Lightgreen module
a)
b)
c)
38
Figure S10. Visual summary of enriched GO terms associated with differentially expressed genes using
a) GO Slim Classification b) GO graph
a)
b)
39
Figure S11. Visual summary of enriched GO terms associated with genes in Midnight Blue module
correlated with CEC using a) GO Slim Classification b) GO graph
a)
b)
40
Figure S12. Visual summary of enriched GO terms associated with genes in Grey60 module correlated
with FCT using a) GO Slim Classification b) GO graph
a)
b)
41
Figure S13. Visual summary of enriched GO terms associated with genes in Lightgreen module
correlated with FCT using a) GO Slim Classification b) GO graph
a)
b)
42
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