ONLINE ONLY Supplemental material · Endothelial cell culture and RNA quantification Primary human...
Transcript of ONLINE ONLY Supplemental material · Endothelial cell culture and RNA quantification Primary human...
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.
©AANS 2018, except where prohibited by US copyright law
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).