J Neurosurg

SUPPLEMENTAL MATERIAL

ONLINE ONLYSupplemental materialAssociation between low levels of serum miR-638 and atherosclerotic plaque vulnerability in patients with high-grade carotid stenosis Luque et al.https://thejns.org/doi/abs/10.3171/2018.2.JNS171899

DISCLAIMER The Journal of Neurosurgery acknowledges that the following section is published verbatim as submitted by the authors and did not go through either the Journal’s peer-review or editing process.

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Supplemental Information and Tables

Bioinformatics prediction of miR-638 association with stroke

A bioinformatics analysis was conducted to predict the target genes regulated by hsa-

miR-638, using seven different publically available algorithms, including miRWALK

(http://www.umm.uni-heidelberg.de/apps/zmf/mirwalk/index.html), DIANA microT

v5.0 (http://diana.cslab.ece.ntua.gr/microT/), miRDB (http://mirdb.org/miRDB/),

miRanda-mirSVR (http://www.microrna.org/), TargetScan 6.2

(http://www.targetscan.org/), TargetMiner

(http://www.isical.ac.in/~bioinfo_miu/targetminer20.htm) and miRTarBase

(http://mirtarbase.mbc.nctu.edu.tw/index.php) 2. Thus, we obtained 3,516 potential

gene targets for miR-638. To reveal potential miR-638 gene targets relevant in stroke

pathology, we compared our gene list with the SigCS base, an integrated web-based

genetic information resource for human cerebral stroke featuring 1,943 non-redundant

genes 4. Considering all etiologies from the SigCS base, we obtained 358 potential miR-

638 gene targets. Narrowing the search to the “stroke” and “atherosclerosis” etiologies

only (including 302 genes), we found 61 genes as potential miR-638 targets

(Supplementary Table 3). Finally, we employed FatiGO (http://v4.babelomics.org/), a

web tool for finding significant associations of gene ontology terms with groups of

genes 1, to unveil potential pathways, processes and functions involving miR-638

regulation in stroke.

Endothelial cell culture and RNA quantification

Primary human umbilical vein endothelial cells (HUVECs) (Advancell,

Barcelona, Spain) (4 x 105 cells per well in 6-well culture plates) were grown in

endothelial growth medium EGM-Bullet kit (Lonza Ibérica, Spain) supplemented with

10% FCS and maintained at 37 ºC in a 5% CO2 atmosphere. At 70% confluence, cells

were either left untreated or stimulated for 48 h with the pro-inflammatory cytokines

TNF-α (100 U/ml) plus IFN-γ (1000 U/ml) (both from R&D Systems, Minneapolis,

MN, USA). Cell pellets and supernatants were collected and immediately stored at -80

ºC.

Total RNA extraction and real-time RT-PCR quantification of endothelial cell

miR-638 and miR-155 in both cell pellets (10 ng RNA/sample) and cell supernatants (5

µl RNA/sample) were performed as described above using the respective TaqMan

microRNA assays (hsa-miR-638: ID 001582; hsa-miR-155: ID 000479) (Applied

BioSystems). In both cases, the spiked-in cel-miR-54 was also included as control for

normalization. ΔΔCt was calculated by subtracting the ΔCt of (TNF-α+IFN-γ)-

stimulated versus non-stimulated HUVECs. The fold change in miRNA abundance was

calculated with the equation 2-ΔΔCt.

Involvement of miR-638 in vascular pathology

The major constitutive cell types participating in atherosclerotic vascular disease

and contributing to atherogenesis and vulnerable plaque formation are the endothelial

cells and the intimal VSMCs. miR-638 is substantially down-regulated in proliferative

human VSMCs after platelet-derived growth factor (PDGF) stimulation 3. On the other

hand, we found that miR-638 was expressed in cultured endothelial cells. Moreover,

upon pro-inflammatory stimulation, both the intracellular and released miR-638 levels

were reduced compared to those found in non-stimulated cells. Conversely, pro-

inflammatory cytokines up-regulated the endothelial expression and increased the

release of the multi-functional miR-155, as previously described 5 (Supplementary Fig.

1).

Furthermore, 61 miR-638 potential target genes, according to different miRNA

target prediction algorithms, could be related to the “stroke” and “atherosclerosis”

etiologies using the SigCS base as reference 4 (Supplementary Table 3). An unbiased

functional enrichment analysis of this gene set using the FatiGO tool confirmed miR-

638 as the only miRNA significantly represented (p< 0.05), and predicted functional

genes, pathways and biological processes related to stroke and significantly regulated

by miR-638 (Supplementary Table 4).

References

1. Al-Shahrour F, Diaz-Uriarte R, Dopazo J: FatiGO: a web tool for finding

significant associations of Gene Ontology terms with groups of genes.

Bioinformatics 20:578-580, 2004

2. He S, Zeng S, Zhou ZW, He ZX, Zhou SF: Hsa-microRNA-181a is a regulator

of a number of cancer genes and a biomarker for endometrial carcinoma in

patients: a bioinformatic and clinical study and the therapeutic implication.

Drug Des Devel Ther 9:1103-1175, 2015

3. Li P, Liu Y, Yi B, Wang G, You X, Zhao X, et al: MicroRNA-638 is highly

expressed in human vascular smooth muscle cells and inhibits PDGF-BB-

induced cell proliferation and migration through targeting orphan nuclear

receptor NOR1. Cardiovasc Res 99:185-193, 2013

4. Park YK, Bang OS, Cha MH, Kim J, Cole JW, Lee D, et al: SigCS base: an

integrated genetic information resource for human cerebral stroke. BMC Syst

Biol 5 (Suppl 2): S10, 2011

5. Wu XY, Fan WD, Fang R, Wu GF: Regulation of microRNA-155 in endothelial

inflammation by targeting nuclear factor (NF)-kappaB P65. J Cell Biochem

115:1928-1936, 2014

Supplementary Fig. 1.- Pro-inflammatory stimuli modulate the expression and

release of miR-638 and miR-155 in cultured HUVECs. The relative levels of

intracellular and extracellular miR-638 and miR-155 are expressed as Fold Change

(log2) (Log2 FC) of (TNF-α+IFN-γ)-s timulated versus non-stimulated HUVECs, and

are given as mean values + SD from triplicate experiments. The intracellular and

extracellular levels of each miRNA are not comparable.

Supplementary Table 1. Main clinical characteristics of CEA patients at 0 and 5 years post-intervention

Characteristics n CEA (±SD) / (%) n post CEA (5 years) (±SD) / (%) pvalue

Age (years) 9 64 8.0 9 69 8.0 - Sex (% Male) 9 9 100.0 9 9 100.0 - Smoking 9 4 44.4 9 0 0.0 0.04 Dyslipidemia 9 4 44.4 9 5 55.6 0.50 HTN 9 8 88.9 9 8 88.9 1.00 Peripheral vasc. 9 2 22.2 9 2 22.2 1.00 CAD 9 1 11.1 9 2 22.2 0.50 Total cholesterol (mmol/l) 9 5.2 0.7 9 4.7 1.1 0.26 LDL-C (mmol/l) 9 3.4 0.7 9 2.6 1.0 0.07 HDL-C (mmol/l) 9 1.1 0.2 9 1.3 0.3 0.12 TG (mmol/l) 9 1.5 0.5 9 1.8 1.3 0.46 WBC x 106/l 9 6679 1267 9 7460 2285 0.38 Antiplatelet treatment 9 8 88.9 9 7 77.8 0.50

Cholesterol treatment 9 3 33.3 9 6 66.7 0.17 CAD: coronary artery disease; HDL-C: high density lipoprotein cholesterol; HTN: hypertension; LDL-C: low density lipoprotein cholesterol; TG: triglycerides; WBC: white blood cells. Data are reported as a mean (±SD) or n (%). Bold font: statistically significant values (p <0.05).

Supplementary Table 2. Main clinical characteristics of the stroke CEA patients

Characteristics n CEA (±SD) / (%) n Controls (±SD) / (%) pvalue

Age (years) 11 64.7 13.6 36 67.7 13.4 0.52 Sex (% Male) 11 11 100.0 36 21 58.3 0.00 Smoking 11 8 72.7 36 10 27.8 0.01 Dyslipemia 11 6.0 54.5 36 16 44.4 0.40 HTN 11 10 90.9 36 21 58.3 0.05 Diabetes 11 4 36.4 36 8 22.2 0.28 Peripheral vasc. 11 2 18.2 36 5 13.9 0.53 CAD 11 2 18.2 36 1 2.8 0.13 Ischemic Stroke 11 11 100.0 36 0 0.0 0.00 Bilateral pathology > 50* 11 5 45.5 36 6.0 16.7 0.06 Fibrinogen (g/l) 11 5.0 1.8 36 4.3 1.2 0.12 Total cholesterol (mmol/l) 11 4.7 1.5 36 3.7 1.2 0.02 LDL-C (mmol/l) 11 2.5 1.4 36 2.2 0.7 0.25 HDL-C (mmol/l) 11 1.3 0.7 36 1.2 0.3 0.83 TG (mmol/l) 11 1.6 0.5 36 1.9 1.0 0.30 ESR (mm/h) 11 15.5 14.2 35 14.7 10.9 0.85

WBC x 106/l 11 7065 1387 30 7744 2597 0.41 Creatinine (mg/dl) 11 0.8 0.2 34 1.0 1.0 0.52 SBP (mm Hg) 11 169.5 27.9 36 139.2 36.8 0.02 Antiplatelet treatment 9 8 88.9 36 11 30.6 0.00 SBP treatment 11 10 90.9 36 18 50.0 0.02

Cholesterol treatment 10 7 70.0 36 14 38.9 0.08 CAD: coronary artery disease; ESR: erythrocyte sedimentation rate; HDL-C: high density lipoprotein cholesterol; HTN: hypertension; LDL-C: low density lipoprotein cholesterol; SBP: systolic blood pressure; TG: triglycerides; WBC: white blood cells. (*) More than 50% contralateral stenosis on ultrasound. Data are reported as a mean (±SD) or n (%). Bold font: statistically significant values (p < 0.05).

Supplementary Table 3. Potential miR-638 target genes from the SigCS database (“stroke” and

“atherosclerosis” etiologies)

Gene Symbol RefseqID Description

ACCN2 NM_001040467 amiloride-sensitive cation channel 2, neuronal

ACVRL1 NM_000020 activin A receptor type II-like 1

ADD1 NM_001119 adducin 1 (alpha)

ASS1 NM_000050 argininosuccinate synthase 1

BCKDHB NM_000056 branched chain keto acid dehydrogenase E1, beta polypeptide

CACNA1A NM_001252059 calcium channel, voltage-dependent, P/Q type, alpha 1A subunit

CACNA1C NM_000719 calcium channel, voltage-dependent, L type, alpha 1C subunit

CHD5 NM_015557 chromodomain helicase DNA binding protein 5

CNR1 NM_001160226 cannabinoid receptor 1 (brain)

CX3CR1 NM_001171171 chemokine (C-X3-C motif) receptor 1

CYP11B1 NM_000497 cytochrome P450, family 11, subfamily B, polypeptide 1

CYP19A1 NM_000103 cytochrome P450, family 19, subfamily A, polypeptide 1

DBT XM_005270545 dihydrolipoamide branched chain transacylase E2

DLG4 XM_005256489 discs, large homolog 4 (Drosophila)

DMPK NM_001081560 dystrophia myotonica-protein kinase

ECE1 NM_001113347 endothelin converting enzyme 1

EPHX2 NM_001256482 epoxide hydrolase 2, cytoplasmic

ESR1 NM_000125 estrogen receptor 1

F13A1 NM_000129 coagulation factor XIII, A1 polypeptide

GAA NM_000152 glucosidase, alpha; acid

GHR NM_000163 growth hormone receptor

GRIN1 NM_000832 glutamate receptor, ionotropic, N-methyl D-aspartate 1

GRIN2B NM_000834 glutamate receptor, ionotropic, N-methyl D-aspartate 2B

HABP2 NM_001177660 hyaluronan binding protein 2

HFE NM_000410 hemochromatosis

HMCN1 NM_031935 hemicentin 1

HNF1A NM_000545 HNF1 homeobox A

HSD11B2 NM_000196 hydroxysteroid (11-beta) dehydrogenase 2

ITGA2 NM_002203 integrin, alpha 2 (CD49B, alpha 2 subunit of VLA-2 receptor)

JAK2 NM_004972 Janus kinase 2

KALRN NM_001024660 kalirin, RhoGEF kinase

KRT14 NM_000526 keratin 14

LDLR NM_000527 low density lipoprotein receptor

LEP NM_000230 leptin

LPIN1 NM_001261427 lipin 1

LPIN2 NM_014646 lipin 2

MTHFR NM_005957 methylenetetrahydrofolate reductase (NAD(P)H)

MYO7A NM_000260 myosin VIIA

NF1 NM_000267 neurofibromin 1

NLRP3 NM_001079821 NLR family, pyrin domain containing 3

NOS1 NM_000620 nitric oxide synthase 1 (neuronal)

NOTCH3 NM_000435 notch 3

PCNT NM_006031 pericentrin

PDGFRA XM_006714041 platelet-derived growth factor receptor, alpha polypeptide

PLAU NM_001145031 plasminogen activator, urokinase

PRKAR1A NM_001276289 protein kinase, cAMP-dependent, regulatory, type I, alpha

PTGIS NM_000961 prostaglandin I2 (prostacyclin) synthase

SCN5A NM_000335 sodium channel, voltage-gated, type V, alpha subunit

SERPINA3 NM_001085 serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 3

SLC1A2 NM_001195728 solute carrier family 1 (glial high affinity glutamate transporter), member 2

SRR NM_001304803 serine racemase

TBXA2R NM_001060 thromboxane A2 receptor

TLR4 NM_003266 toll-like receptor 4

TNF NM_000594 tumor necrosis factor

TNFRSF11B NM_002546 tumor necrosis factor receptor superfamily, member 11b

TNFRSF1B NM_001066 tumor necrosis factor receptor superfamily, member 1B

TNXB NM_019105 tenascin XB

TRAK1 NM_001042646 trafficking protein, kinesin binding 1

UCP2 NM_003355 uncoupling protein 2 (mitochondrial, proton carrier)

WNK4 NM_032387 WNK lysine deficient protein kinase 4

XYLT1 NM_022166 xylosyltransferase I

Supplementary Table 4. Functional profiling (FatiGO) output from 61 potential miR-638 target genes relevant in stroke and atherosclerosis etiologies according to the SigCS database (Supplementary Table 3)

Jaspar TFBS Term Odds ratio (loge) pvalue Adj_pvalue

Mafb 24.165 0.00003059 0.001897

miRNA target Term Odds ratio (loge) pvalue Adj_pvalue

hsa-miR-638 1.948 0.000005946 0.00418

Biocarta Term Odds ratio (loge) pvalue Adj_pvalue

Nitric Oxide Signaling Pathway 48.444 9.36E-05 0.00002547

Chaperones modulate interferon Signaling Pathway 50.251 0.000002669 0.000363

Eicosanoid Metabolism 43.316 0.00001545 0.0014

Growth Hormone Signaling Pathway 41.083 0.00002802 0.001906

SODD/TNFR1 Signaling Pathway 47.361 0.0002337 0.01271

Acetylation and Deacetylation of RelA in the nucleus 46.025 0.0002917 0.01322

Fibrinolysis Pathway 44.847 0.0003559 0.01383

Regulation of transcriptional activity by PML 42.839 0.000503 0.0171

NF-kB Signaling Pathway 41.168 0.0006749 0.0204

Reactome Term Odds ratio (loge) pvalue Adj_pvalue

Synaptic Transmission (REACT_13685) 29.923 0.00000969 0.0006395

Hormone biosynthesis (REACT_15314) 31.514 0.0003914 0.01292

KEGG Term Odds ratio (loge) pvalue Adj_pvalue

Amyotrophic lateral sclerosis (ALS) (hsa05014) 39.171 7.58E-06 0.00000147

Cytokine-cytokine receptor interaction (hsa04060) 25.584 6.06E-04 0.00005877

Calcium signaling pathway (hsa04020) 27.081 0.000006234 0.0004031

Adipocytokine signaling pathway (hsa04920) 32.024 0.00003444 0.00167

Alzheimer’s disease (hsa05010) 25.878 0.00006242 0.002422

Neuroactive ligand-receptor interaction (hsa04080) 21.481 0.0001283 0.004148

Type II diabetes mellitus (hsa04930) 33.418 0.0002303 0.006381

C21-Steroid hormone metabolism (hsa00140) 30.079 0.0005838 0.01416

MAPK signaling pathway (hsa04010) 20.113 0.0008219 0.01595

Long-term potentiation (hsa04720) 28.973 0.0007945 0.01595

InterPro Term Odds ratio (loge) pvalue Adj_pvalue

Voltage-dependent calcium channel, alpha-1 subunit 42.137 0.00002112 0.02281

EGF-like, type 3 25.243 0.00001705 0.02281

Supplementary Table 5. Main clinical characteristics of high cardiovascular risk individuals (SCORE >5) (symptomatic CEA patient group versus non-CEA control group)

Characteristics n CEA (±SD) / (%) n Control (±SD) / (%) pvalue Age (years) 8 74.0 5.4 14 72.6 8.1 0.68 Sex (% Male) 8 8 100 14 12 85.7 0.26 Smoking 8 5 62.5 14 7 50.0 0.57 Dyslipemia 8 4 50.0 14 5 35.7 0.51

HTN 8 7 87.5 14 11 78.6 0.60 Diabetes 8 3 37.5 14 6 42.9 1.00 Peripheral vasc. 8 2 25.0 14 3 21.4 0.85

CAD 8 2 25.0 14 0 0.0 0.05

Cerebrovascular event 8 8 100 14 0 0.0 0.00 Bilateral pathology > 50* 8 4 50.0 14 5 35.7 0.51

Fibrinogen (g/l) 8 4.7 1.7 14 4.9 1.0 0.76

Total cholesterol (mmol/l) 8 4.1 1.3 14 4.2 1.1 0.84

LDL-C (mmol/l) 8 1.9 1.0 14 2.4 0.7 0.04 HDL-C (mmol/l) 8 1.3 0.8 14 1.2 0.2 0.56

TG (mmol/l) 8 1.2 0.5 14 2.2 1.1 0.04 ESR (mm/h) 7 16.4 16.8 14 16.9 9.8 0.93 WBC x 106/l 8 7384 1422 14 7323 1806 0.93

Creatinine (mg/dl) 8 0.8 0.1 13 0.9 0.6 0.74

SBP (mm Hg) 8 166.3 29.4 14 159.7 34.1 0.65

Antiplatelet treatment 7 6 85.7 14 6 42.9 0.06

SBP treatment 8 7 87.5 14 9 64.3 0.24

Cholesterol treatment 7 6 85.7 14 5 35.7 0.03 CAD: coronary artery disease; ESR: erythrocyte sedimentation rate; HDL-C: high density lipoprotein cholesterol; HTN: hypertension; LDL-C: low density lipoprotein cholesterol; SBP: systolic blood pressure; TG: triglycerides; WBC: white blood cells. (*) More than 50% contralateral stenosis on ultrasound. Data are reported as a mean (±SD) or n (%). Bold font: statistically significant values (p < 0.05).