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Thrombosis Research
journal homepage:www.elsevier.com/locate/thromres
Full Length Article
Expression pro
files of the internal jugular and saphenous veins: Focus on
hemostasis genes
Nicole Ziliotto
a, Silvia Meneghetti
a, Erica Menegatti
b, Marcello Baroni
a, Barbara Lunghi
a,
Fabrizio Salvi
c, Manuela Ferracin
d, Alessio Branchini
a, Donato Gemmati
e, Francesco Mascoli
f,
Paolo Zamboni
b, Francesco Bernardi
a,⁎, Giovanna Marchetti
eaDepartment of Life Science and Biotechnology, University of Ferrara, Ferrara, Italy bDepartment of Morphology, Surgery and Experimental Medicine, University of Ferrara, Italy
cCenter for Immunological and Rare Neurological Diseases, Bellaria Hospital, IRCCS of Neurological Sciences, Bologna, Italy dDepartment of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Bologna, Italy eDepartment of Biomedical and Specialty Surgical Sciences, University of Ferrara, Ferrara, Italy
fUnit of Vascular and Endovascular Surgery, S. Anna University-Hospital, Ferrara, Ferrara, Italy
A R T I C L E I N F O Keywords: Hemostasis Gene expression Jugular vein Saphenous vein Microarray Venous bed A B S T R A C T
Introduction: Venous bed specificity could contribute to differential vulnerability to thrombus formation, and is potentially reflected in mRNA profiles.
Materials and methods: Microarray-based transcriptome analysis in wall and valve specimens from internal ju-gular (IJV) and saphenous (SV) veins collected during IJV surgical reconstruction in patients with impaired brain outflow. Multiplex antigenic assay in paired jugular and peripheral plasma samples.
Results: Most of the top differentially expressed transcripts have been previously associated with both vascular and neurological disorders. Large expression differences of HOX genes, organ patterning regulators, pinpointed the vein positional identity.
The“complement and coagulation cascade” emerged among enriched pathways. In IJV, upregulation of genes for coagulation inhibitors (TFPI, PROS1), activated protein C pathway receptors (THBD, PROCR),fibrinolysis activators (PLAT, PLAUR), and downregulation of thefibrinolysis inhibitor (SERPINE1) and of contact/ampli-fication pathway genes (F11, F12), would be compatible with a thromboprotective profile in respect to SV. Further, in SV valve the prothrombinase complex genes (F5, F2) were up-regulated and the VWF showed the highest expression. Differential expression of several VWF regulators (ABO, ST3GAL4, SCARA5, CLEC4M) was also observed. Among other differentially expressed hemostasis-related genes, heparanase (HPSE)/heparanase inhibitor (HPSE2) were up-/down-regulated in IJV, which might support procoagulant features and disease conditions.
The jugular plasma levels of several proteins, encoded by differentially expressed genes, were lower and highly correlated with peripheral levels.
Conclusions: The IJV and SV rely on differential expression of many hemostasis and hemostasis-related genes to balance local hemostasis, potentially related to differences in vulnerability to thrombosis.
1. Introduction
The analysis of mRNA expression profiles of human vascular spe-cimens has been pursued more in the arteries than in the veins, prompted by the association with pathological phenotypes [1]. The ample heterogeneity of veins, related to their specific role and position [2], is expected to produce variation in the transcriptional profile.
The expression profile of human saphenous veins (SV) has received
attention mostly in relation to vein graft disease, being the SV a com-monly used conduit in bypass graft surgery. SV has been compared with several arteries, in coronary cultured smooth muscle cells (SMC) [3] and endothelial cells [4], and in cultured SMC from the internal mammary [5] and thoracic arteries [6].
Recently, the transcriptome of SV valve versus non valve explant area has been investigated [7].
The internal jugular vein (IJV), which has a major role in cerebral
https://doi.org/10.1016/j.thromres.2020.04.039
Received 7 February 2020; Received in revised form 15 March 2020; Accepted 27 April 2020
⁎Corresponding author at: University of Ferrara, Department of Life Science and Biotechnology, Via Fossato di Mortara 74, 44121 Ferrara, Italy.
E-mail address:ber@unife.it(F. Bernardi).
Thrombosis Research 191 (2020) 113–124
Available online 03 May 2020
0049-3848/ © 2020 Elsevier Ltd. All rights reserved.
venous return towards the heart, differs from SV for morphological and hemodynamic characteristics [8] that have been studied in patients [9]. The IJV wall expression pattern has been limitedly investigated at protein [10] and mRNA [11] levels in MS patients with disturbed brain outflow. The expression of IJV wall has never been compared either with other veins or with IJV valve.
In different sites of the vascular tree, transcription of different sets of anticoagulants and procoagulant genes contributes to local hemos-tasis balance [12]. In turn, combinations of factors, expressed by the vessel wall and present in the circulating blood, could modulate the ability of the individual vascular bed to counteract potentially pro-thrombotic stimuli [13–16]. Variations of hemostasis gene expression have been reported in cultured endothelial or smooth muscle cells, and in intact vessel explants in response to stimuli/perturbations mimicking flow properties and forces exerted by blood on the vessel wall [17–19]. The intrinsic limitations of cellular and animal models permit only cautious translation of findings in human physiological and patholo-gical conditions [12].
In the present study, we compare in patients with disturbed brain outflow the expression profiles of wall and valve of IJV with those of SV, aimed at defining the most significant differences, particularly in the hemostasis transcriptome.
2. Materials and methods
2.1. Patients and tissue specimens/sampling
Patients affected by multiple sclerosis (MS), who underwent surgical reconstruction of IJV by patch angioplasty with the autologous great SV [20], were included in the present study. Patients were characterized before surgery for IJVflow disturbance by high resolution Doppler ul-trasound and magnetic resonance venography (MRV) [21]. Inclusion and exclusion criteria for the surgery are reported elsewhere [20]. Specimens were obtained from 12 patients (4 males, 8 females, mean age 47.9 ± 6.6) on the day of the surgery: i) IJV wall at the junction with the subclavian vein, ii) SV wall from a short segment removed at the saphenofemoral junction, which was used for jugular patching, and iii) IJV valve and iv) SV valve. Blood samples (one from the IJV and the other from the arm) were obtained from 17 patients (8 males, 9 fe-males, mean age 45.6 ± 8.1) on the day of the surgery.
Peripheral blood samples were also collected from 21 matched healthy subjects (10 males, 11 females, mean age 43.9 ± 8.3). This study was approved by local Ethical Committee (University-Hospital of Ferrara) with protocol number 7-2010. All the recruited subjects signed an informed consent.
2.2. RNA profiling
Specimens retrieved at surgery were placed into RNAlater (Ambion Inc., Austin, TX, US) and stored at−80 °C until RNA extraction. Total RNA was isolated from each homogenized tissue specimen (TRIZOL Reagent, Invitrogen Carlsab, CA, US) using the miRNeasy Mini Kit (Quiagen, Hilden, Germany). Initially, the quality of RNA was verified by agarose gel electrophoresis and spectrophotometer measurement of the A260/A280 ratio and only those samples showing clear ribosomal RNA bands and A260/A280 ratio around 2.0 were further processed. After the additional quality assessment by Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, US), based on the yield and quality recovered, RNAs from IJV walls (n = 4), SV walls (n = 4), IJV valves (n = 4) and SV valves (n = 4) were suitable for the microarray-based transcriptome analysis.
Labelled cRNA was synthesized from 100 ng of total RNA using the Low RNA Input Linear Amplification Kit (Agilent Technologies) in the presence of cyanine 3-CTP (Perkin-Elmer Life Sciences, Boston, MA, US). Hybridization on Agilent whole human genome oligo microarray (Cat.No. G4851A, Agilent Technologies), which represents 60,000
unique human transcripts, was performed in accordance to manufac-turer's indications. Microarray raw-data were obtained with Feature Extraction software v.10.7 (Agilent Technologies) and analyzed by using the GeneSpring GX v.14 software (Agilent Technologies). Principal component analysis was performed on the normalized data to evaluate similarities or differences among each specimen group. 2.3. Plasma protein assays
Plasma samples were isolated from blood drawn in sodium-citrate tubes at the time of the surgery. Aliquots were stored at−80 °C until use.
Soluble endothelial protein C receptor (sEPCR) levels were de-termined by a commercial ELISA consisting of goat polyclonal anti-bodies (Human EPCR DuoSet, DY2245, R&D Systems Inc., Minneapolis, MN, US) [22]. Samples were diluted 1:50 and assayed in duplicate for sEPCR antigen quantification.
A sandwich ELISA was developed for detection of total protein S (PS) [22,23]. Polyclonal sheep anti-human PS antibody (Haematologic Technologies, Essex Junction, VT, US) was coated to microtiter plate (Nunc MaxiSorpflat-bottom, Thermo Fisher Scientific, Waltham, MA, US). Bound PS, from 1:4000 diluted plasma samples, was detected with polyclonal rabbit anti-PS antibody (Dako, Glostrup, Denmark) and with a polyclonal goat peroxidase-conjugated antibody.
Total plasminogen activator inhibitor-1 (PAI-1) and leptin (LEP) levels were assayed using Milliplex™ magnetic bead kits (human neu-rodegenerative disease panel 3, HNDG3MAG-36K, and human angio-genesis panel 1, HAGP1MAG-12K, respectively, Merck Millipore, Germany), while soluble thrombomodulin (sTM) levels were assayed using Luminex Screening Assays magnetic bead kits (Luminex R&D Systems Inc., Minneapolis, MN, US) following the manufacturer's in-structions [24]. Data were acquired using the Luminex® 100 system and analyzed using Bioplex Manager Software version 6.0 (both from Biorad Laboratories, Hercules, CA, US).
Concentrations were calculated according each standard curve generated for the specific target. The intra-assay coefficients of varia-bility for each protein were calculated by 3 independent samples, quantified in triplicate and were as follow: LEP 2.8%, sEPCR 4.4%, PS 1.6%, PAI-1 7.3%, sTM 3.3%.
The evaluation of von Willebrand factor (VWF) multimers in plasma was conducted by a commercial kit (Sebia, Lisses, France) as previously described [25].
2.4. Reverse transcription and quantitative PCR (RT-qPCR)
Reverse transcription was performed with 150 ng of total RNA using the M-MLV Reverse Transcriptase (Thermo Fisher Scientific, Waltham, MA, US), a mixture of oligo(dT) and random primers [11].
expression index of 2−ΔΔCt, in which ΔΔCt is the mean of (Cttarget
-Cthousekeeping) from IJV walls minus the mean of (Cttarget-Cthousekeeping)
from SV walls. Values were expressed as mean fold change ± standard error of the mean.
2.5. Statistical analysis
Afilter on low gene expression was used to keep only the probes expressed in at least one sample (flagged as Marginal or Present). Then, samples were grouped in accordance to bed compartment (IJV-SV) and compared. To evaluate similarities or differences among each group (IJV-SV walls, IJV-SV valves, IJV wall-valve, SV wall-valve) principal component analysis was performed on the normalized data using the GeneSpring GX v.14 software (Agilent Technologies). Differentially expressed genes were selected as having a 2-fold expression difference between their geometrical mean in the two groups by a moderate t-test, followed by the application of Benjamini-Hochberg multiple testing correction with a statistically significant P-value < 0.05. Differentially expressed genes were employed for Cluster Analysis of samples, using the Manhattan correlation as a measure of similarity. Functional cate-gorization was assigned using Gene Ontology (GO) by free access DAVID Bioinformatics database 6.8.
The Shapiro-Wilk test was used to test the normal distribution of protein concentrations. Pearson's test or Spearman's rank test were used to assess correlation between jugular and peripheral plasma levels of the investigated proteins, according to data normality. Differences in protein concentrations between peripheral and jugular plasma were analyzed according to data distribution, using paired t-test or Wilcoxon matched-pair signed rank test. Differences in protein concentrations in peripheral plasma samples between MS patients and healthy subjects were analyzed by Mann-Whitney U test. In the qPCR, mean values of gene expression levels between IJV wall and SV wall were compared by t-test. In all tests, a P-value≤0.05 was considered significant. Statistical analyses andfigures were produced by GraphPad Prism version 6.01 (GraphPad Software, Inc. La Jolla, CA, US).
3. Results
3.1. Transcriptome analysis
Transcriptome analysis detected clearly different expression profiles (fold change≥2, multiple testing correction P < 0.05,Fig. 1) between IJV and SV walls (3375 genes/transcripts), and between IJV and SV valves (395 genes/transcripts). Microarray data are reported in the Supplemental Files S1 (wall analysis) and S2 (valve analysis).
In walls comparison, functional annotation with Gene Ontology analysis (Table SI) revealed a high number of significantly enriched biological processes and pathways. The analysis between valves in-dicated a lower number of differences as compared with walls, and revealed a few pathways and only one significantly enriched process (anterior/posterior pattern specification) (Table SI). Among the genes displaying the most significant differences between walls (Table 1), ten belonged to eight over-represented biological processes.
IJV and SV, both walls and valves, were marked by distinct ex-pression profiles of several HOX genes, master regulators of organ patterning (Fig. 2). In particular, HOXB5 showed the most ample and significant up-regulation (fold change 12.7, P = 6.24E−5 between walls; fold change11, P = 0.003, between valves; Supplemental File S1), and HOXA9 the most significant down-regulation (fold change 130.7, P = 8.6E−05, between walls; fold change 102.6, P = 7.6E−04, between valves; Supplemental File S1). Further, HOXB5 and HOXA9, and in addition HOXA10, HOXB6, HOXC10 and HOXC12 belonged to the top ten differentially expressed genes, in wall or valve comparison (Tables 1 and 2).
For genes displaying the most significant expression differences (Tables 1 and 2), association with disease/dysfunction was explored in literature data, with reference to vascular and neurological conditions. Potential clinical associations were inferred for most of these genes, 16 in wall comparison (Table 1) [26–41] and 14 in valve comparison (Table 2) [31–35,42–48]. In particular, 12 genes (6 in wall and 6 in valve comparisons) have been associated with vascular diseases, and 17 genes with neurological diseases (10 in wall and 7 in valve compar-isons). To note, for 7 genes, the association with both vascular and neurological disorders has been reported.
Fig. 1. Transcriptome analysis of internal jugular vein (IJV) wall versus saphenous vein (SV) wall.
3.2. Expression of hemostasis genes
Among genes up regulated in the IJV wall, the pathway “comple-ment and coagulation cascades” emerged (P < 1.0E−4) as one of the top ten enriched terms (Table SI). For focused analysis of hemostasis profile expression, a panel of genes encoding for factors/cofactors, ac-tivators/inhibitors with established roles in hemostasis, coagulation andfibrinolysis, was selected (Table SII) [49].
Several significant differences were found (Fig. 3), which are schematically depicted in Fig. 4. In IJV wall vs SV wall, increased mRNA levels of coagulation cascade inhibitors of the tissue factor and the activated protein C (APC) pathways were detected (Fig. 3A). The differentially expressed transcripts encoded for the tissue factor pathway inhibitor (TFPI), the receptor of thrombin (thrombomodulin, THBD), the endothelial protein C receptor (PROCR) and the protein S, cofactor of both TFPI and APC (PROS1). Inspection of hybridization fluorescence levels in the microarrays permitted to compare the ex-pression levels in the compartments (Fig. 3B).
The multiple probes detecting single genes revealed reproducible differences: i) for TFPI, three probes detected a significantly lower ex-pression in SV wall than in IJV wall. Quantitative real time PCR
(RT-qPCR) supported the higher expression of TFPI in IJV wall (mean fold change ± SEM, 2.9 ± 0.7; ΔCT mean ± SD, 3.3 ± 0.43 vs 4.85 ± 0.77); ii) for PROS1, two probes displayed highly coherent patterns indicating upregulation in IJV, which was also suggested by RT-qPCR (mean fold change ± SEM, 2.22 ± 0.99;ΔCT mean ± SD, 6.45 ± 0.46 vs 7.60 ± 1.77); iii) for F13A1, the higher expression in IJV wall was supported by two probes.
Other coagulation genes with low expression levels in the vascular specimens, as indicated by lower log2values, showed differences
be-tween SV wall and valve (Fig. SI): i) higher expression of contact phase-intrinsic coagulation genes, F12 and F11, and ii) lower expression of the common pathway genes, F5 and F2.
VWF showed the highest expression levels among the hemostasis genes, in all vascular specimens (Fig. SI), particularly in the SV valve. Two pro-fibrinolytic genes, the tissue plasminogen activator (PLAT) and the receptor for the urokinase plasminogen activator (PLAUR), were more expressed in the jugular than in the saphenous compartment (Fig. 3). Conversely SERPINE1, coding for the inhibitor of the tissue plasminogen activator (PAI-1), was less expressed in the IJV. For PLAT, the RT-qPCR (mean fold change ± SEM, 1.73 ± 0.81;ΔCT mean ± SD, 5.57 ± 1.38 vs 6.63 ± 0.96) did not parallel the microarray
Table 1
Best hits (according to P value) in the internal jugular vein wall vs saphenous vein wall transcriptome analysis.
P-value Fold change Gene symbol/name Protein localization/function–Biological process
Potential effect/disease association (vascular Δ, neurological □ diseases). A) up regulated genes-Top 10
1.61E−05 23.8 MATN4/matrilin 4 Extracellular matrix component–extracellular matrix organizationb
2.76E−05 5.2 TSHZ2/teashirt zincfinger homeobox2 Transcription factor/regulation of transcription–multicellular organism developmentb
4.34E−05 6.4 PLBD1/phospholipase B domain containing 1
Phospholipase activity–lipid catabolic process, phospholipid metabolism Stroke-related transcriptΔ [26]
4.48E−05 4.4 NINJ2/ninjurin 2 Cell surface protein–cell adhesionb, tissue regeneration
SNPs associated with ischemic strokeΔ [27], SNPs associated with MS□ [28]
5.37E−05 10.7 SIX2/SIX homeobox 2 Transcription factor–positive regulation of transcriptionb, anterior/posterior axis specification
5.60E−05 3.38 SIRPA/signal-regulatory protein alpha Receptor for CD47–cell adhesionb, leucocyte migrationb, intracellular signaling during synaptogenesis
and synaptic function
5.81E−05 9.4 FBXO2/F-box protein 2a Subunit of the ubiquitin protein ligase complex–ubiquitination process, negative regulation of cell
proliferationb
6.07E−05 10.1 TREML1/triggering receptor expressed on myeloid cells-like 1
Receptor–vascular homeostasis, regulation of immune responseb, platelet activationb, protects against
inflammation-associated hemorrhage Δ [29] increased level as a potential protection mechanism for AD□ [30]
6.24E−05 3.2 AGPAT9/1-acylglycerol-3-phosphate O-acyltransferase 9
Acyltransferase activity–phospholipids biosynthetic process, synthesis of lung surfactant and PAF 6.24E−05 12.7 HOXB5/homeobox B5a Transcription factor–positive regulation of transcriptionb, anterior/posterior pattern specification
Vascular remodeling in a cytokine-dependent mannerΔ [31] B) down regulated genes-Top 10
4.86E−07 123.8 HOXA10/Homeobox A10a Transcription factor–anterior/posterior pattern specification
2.51E−06 130.6 HOTAIR/HOX transcript antisense RNA, lncRNAa
Regulator of HOXD gene cluster
Post-ischemia myocardial remodelingΔ [32], pathogenesis of MS□ [33], prognostic marker of glioma□ [34] 7.25E−06 151.3 HOXA11AS/HOXA11 antisense RNA,
ncRNAa
Malignant progression of glioma□ [35] 7.25E−06 6.9 WNK2/WNK lysine deficient protein kinase
2
Protein kinase activity–intracellular signal transductionb, regulation of electrolyte homeostasis
Tumor suppressor, meningiomas and glioma□ [36]
9.10E−06 78.73 HOXC10/Homeobox C10a Transcription factor–anterior/posterior pattern specification
Up regulated in glioblastoma□ [37]
1.16E−05 51.6 HPSE2/Heparanase 2a Extracellular protein without heparanase activity–extracellular matrix organizationbangiogenesis,
inhibitor of heparanase activity, tumor suppression
Protective role in microvascular inflammation Δ [38], upregulated in the brains of AD□ [39] 2.50E−05 17.4 SLC8A2/solute carrier family 8 member 2 Sodium-calcium exchanger–cellular calcium ion homeostasis, regulation of cardiac conductionb,
memory, learning
Tumor suppressor gene in glioblastoma□ [40] 3.92E−05 3.7 PBXIP1/pre-B-cell leukemia homeobox
interacting protein 1
Transcription corepressor/microtubule -binding protein–erythroid differentiation, scaffolding protein overexpressed in astrocytomas□ [41]
4.34E−05 39.6 ASB5/ankyrin repeat and SOCS box containing 5
Ubiquitin E3 ligase/post-translational modification –intracellular signal transductionb, regulation of
compartment size 4.34E−05 12.5 C1orf106/chromosome 1 open reading
frame 106
Positive regulation of protein ubiquitination–intestinal epithelial permeability and immune homeostasis
AD, Alzheimer's disease; ECs, endothelial cells; lncRNA, long non-coding RNA; MI, myocardial infarction; MS, multiple sclerosis; ncRNA, non-coding RNA; PAF, platelet activation factor.
a Up (A) or down (B) regulated genes (P < 0.05) both in IJV valve vs SV valve and in IJV wall vs SV wall comparisons.
analysis.
3.3. Expression of hemostasis-related genes
Expression of genes potentially associated with hemostasis protein levels and/or with venous thrombosis (Table SII) [50–55] was also investigated (Fig. 5, and Table SIII). Several modulators of FVIII and/or VWF levels (ABO, CLEC4M, LRP1, SCARA5, STE3GAL4) displayed dif-ferences, part of them in multiple comparisons of specimen groups (Table SIII). SELP encoding for P-selectin, that is expressed in en-dothelial Weibel-Palade bodies together with VWF, was upregulated in IJV wall and in SV valve (Fig. 5A–B).
The protein C/venous thrombosis-associated CADM1 [56] showed higher expression in valves than in walls as supported by two probes.
Among differentially expressed genes involved in hemostasis-asso-ciated processes, several were functionally related. i) F2R and F2RL2, encoding the protease activated receptors PAR1 and PAR3, were less expressed in JV wall than in SV wall (Fig. 5A and Table SIII). ii) Two genes (HPSE and HPSE2) involved in the metabolism of heparan sulfate proteoglycans, and in the initiation of blood coagulation (HPSE) [52], showed marked downregulation in the SV wall (HPSE) or in IJV (HPSE2) (Fig. 5), both wall (P = 1.16E−05, Table 1) and valve (P = 0.008,Table 2) as confirmed by two probes. iii) The LEP gene encoding for leptin, an adipokine with cardiovascular effects, was up regulated in SV wall (Fig. 5B). Differently, the LEP receptor gene (LEPR) was downregulated in the SV wall, as confirmed by multiple probes (Fig. 5B).
3.4. Protein levels in IJV plasma
We selected proteins with endothelial expression acting in the in-hibition of the coagulation/fibrinolysis pathways (sTM, sEPCR, PS and PAI-1), or related to hemostasis/thrombosis (LEP). Concentrations of proteins were compared in paired jugular and peripheral plasma sam-ples (Fig. 6). Highly significant correlation of values was detected for all proteins. Significantly lower levels in jugular than in peripheral plasma were observed for sTM, PS, PAI-1 and LEP (Fig. 6) by paired analysis. Concentrations of the proteins in peripheral plasma were also compared with those from peripheral plasma of healthy subjects (Fig. 6). PS values in healthy subjects were slightly lower than in pa-tients (P = 0.048).
We also investigated VWF multimers in paired jugular and periph-eral plasma samples (Fig. SII). Heterogeneous results were obtained: overlapping profiles for two paired samples (MS21, MS23) or similar multimer distribution with higher protein amounts in the peripheral sample (MS11 and MS22). In one pair (MS19), higher proportion of high molecular weight multimers in the jugular sample and higher amounts of VWF in the peripheral one were detected.
4. Discussion
Markedly distinct mRNA profiles characterized the IJV and SV vascular beds, as supported by thousands of differentially expressed genes. The observed expression profiles would represent an integrated evaluation of cell specific transcript levels in the vessel wall, which is a mixed cell population tissue. Differences in the expression profile might
Fig. 2. Schematic representation of the expression patterns of HOX genes belonging to HOXA, HOXB and HOXC clusters.
stem from: i) the contribution of several types of cells (endothelial, smooth muscle cells, fibroblasts and pericytes), and the relative pro-portion in the wall of IJV as compared to SV; ii) the heterogeneity of vascular smooth muscle cells and endothelial cells and iii) the cell type–dependent response to various stimuli (i.e. changes in blood flow characteristics), which might influence the specific gene expression levels.
The analysis between valves indicated a lower number of differ-ences as compared with walls, which could be related to the lower number of cell types contributing to the valve structure and function.
Thisfirst report on comparison of transcriptomes from vein walls highlights a number of biological processes and pathways, marked by genes with highly significant differences. Interpretation of results is favored by the position of veins in the vascular tree, the IJV with a major role in cerebral venous return towards the heart and the SV that collects blood from the lower limb. Accordingly, the distinct expression profiles of several HOX transcription factors, master regulators of organ patterning [57], were highly coherent with the HOX genes activation along the mammalian anteroposterior development, and thus with the positional identity of these veins.
Among genes displaying the most significant expression differences, the lncRNA HOTAIR and four coding transcripts (NINJ2, TREML1, HPSE2, FNDC5) could offer, in light of previous studies, [9,58]
molecular links between vascular dysfunction and several neurological diseases, including MS and Alzheimer's disease, as summarized in Tables 1 and 2. To note, six down-regulated genes in IJV (HOTAIR, HOXA11AS, WNK2, HOXC10, SCL8A2, LHX9) have been previously associated to gliomas/glioblastoma, brain tumors found to be asso-ciated with venous thromboembolism [59].
Moreover,five transcripts with restricted (PLP1) or main (SIRPA, FBXO2, LRRC7 and CBLN4) expression in brain, as reported in the NCBI database, were upregulated in the IJV. Taking into account that our observations were obtained in vascular tissue, it is tentative to speculate that these transcripts would be also expressed in the brain vasculature. Overall, thesefindings are in favor of the vascular-neuronal network [58] hypothesis, which requires further investigation.
Focused analysis of the hemostasis transcriptome profiles in veins was favored byfinding the “complement and coagulation cascades” among the enriched pathways. The overexpression in the IJV wall with outflow disturbance of several endothelial anticoagulant genes could also imply an anti-inflammatory transcription profile. As a matter of fact both TFPI and APC-pathway members (EPCR, TM, PS) display anti-inflammatory properties [60]. On the other hand, the concomitant up-regulation in the IJV of a number of inflammation genes (n = 88, transcriptome enrichment analysis P = 8.0E−19) suggests the coex-istence of pro-inflammatory and anti-inflammatory components in a
Table 2
Best hits (according to P value) in the internal jugular valve vs saphenous valve transcriptome analysis.
P-value Fold change Gene symbol/name Protein localization/function–Biological process
Potential effect/disease association (vascular Δ, neurological □ diseases) A) up regulated genes - Top 10
0.002 7.01 LRRC7/leucine rich repeat containing 7 Densin/synaptic adhesion molecule, cell junction–neuronal signaling Bipolar disorder□ [42]
0.002 4.6 PLP1/proteolipid protein 1 Component of myelin/stabilization of myelin sheaths- oligodendrocyte development and axonal survival.
PLP mutations in patients with MS□ [43]
0.002 48.1 CBLN4/cerebellin 4 precursora Secreted protein/trans-neuronal cytokine–synapse formation and plasticity
0.002 4.4 FNDC5/fibronectin type III domain containing 5-a
Irisin/adipomyokine–energy expenditure, mitigation of oxidative stress and systemic inflammatory state
Prognostic factor in several cardiovascular diseasesΔ [44], synapse plasticity and memory rescue in AD models □ [45]
0.002 8.7 TMEM255A/transmembrane protein 255Aa
Transmembrane protein
0.003 11.0 HOXB5/homeobox B5a Transcription factor–anterior/posterior pattern specification, regulation of angioblasts and ECs
differentiation from mesoderm-derived precursors Vascular remodeling in a cytokine-dependent mannerΔ [31] 0.003 6.0 HOXB6/homeobox B6a Transcription factor–anterior/posterior pattern specification
0.003 41.6 IRX1/iroquois homeobox 1a Transcription factor/negative regulation of transcription–tumor suppressor gene
IRX1 mutations in congenital heart diseaseΔ [46] 0.006 2.7 HCG11/HLA complex group11 lncRNA Tumor suppressor gene
0.007 3.3 SEC14L6/SEC14 like lipid binding 6a Transporter activity–interactions between proteins and specific phospholipids, lipid metabolism and
signaling B) down regulated genes - Top 10
6.65E−04 36.8 HOXA11AS/HOXA11 antisense RNA, lncRNAa
Malignant progression of glioma□ [35]
7.55E−04 102.6 HOXA9/homeobox A9a Transcription factor–anterior-posterior pattern specificationb
Downregulated expression in coronary vs iliac arteries ECsΔ [47] 7.55E−04 8.7 LHX9/LIM homeobox 9a Transcription factor–differentiation of several neural cell types
Reduced expression in malignant glioma□ [48]
0.001 39.1 HOXA10/homeobox A10a Transcription factor–anterior-posterior pattern specificationb
0.002 46.0 HOTAIR/HOX transcript antisense RNA, lncRNAa
Regulator of HOXD gene cluster
Post-ischemia myocardial remodelingΔ [32], pathogenesis of MS□ [33], prognostic marker of glioma□ [34] 0.002 8.8 HOXC12/homeobox C12a Transcription factor–anterior/posterior pattern specificationb
0.005 5.4 RGS22/regulator of G protein signaling 22a G-protein alpha subunit binding protein–negative regulation of signal transduction
0.008 8.6 HPSE2/heparanase 2a Extracellular protein without heparanase activity–matrix organization and remodeling, angiogenesis,
inhibitor of heparanase activity, tumor suppression
Protective role in microvascular inflammation Δ [38], upregulated in the brains of AD□ [39] 0.008 4.6 PTH2R/parathyroid hormone 2 receptor G-protein-coupled receptor for parathyroid hormone
0.008 3.1 RNF138P/ringfinger protein 138, pseudogene
AD: Alzheimer's disease; EC: endothelial cells; MS: multiple sclerosis.
a Up (A) or down (B) regulated genes both in in IJV valve vs SV valve and in IJV wall vs SV wall (P < 0.05) comparison.
Fig. 3. mRNA expression levels by microarray analysis of hemostasis genes in internal jugular (IJV) and saphenous (SV) veins.
A) Differences between walls of IJV and SV. The genes coding for the coagulation inhibitors are listed (A) according to their position in the coagulation cascade (TFPI and activated protein C pathways).
The P-value of Benjamini-Hochberg multiple testing correction are reported (A and B).
B) Differences among all specimen groups. Mean log2expression values are shown. Error bars indicate the standard deviation. For TFPI, PROS1 and F13A1 expression
disturbedflow region [61].
The expression differences of genes encoding for EPCR, TM, PS and TFPI in human IJV and SV, characterized by ample differences in blood flow, extend to the venous bed previous findings in cultured vascular cells, and/or intact vessel explants exposed to experimental variation of hemodynamic conditions [17,18,62–64]. To note, THBD, encoding for TM, and its transcriptional activator KLF4, known to beflow-regulated [65], were both upregulated in IJV. The differential expression of the genes coding for the coagulation-related receptors PAR1 and PAR3 [51] might be also interpreted in light of hemodynamic forces modulating PAR1 expression in endothelial and VSM cells [66].
Furthermore, modulation of several fibrinolysis genes provides a tight link among hemostasis, inflammation and hemodynamic condi-tions. Hemodynamic features have been found to affect expression of tPA [17–19] and PAI-1 [17,18], suggesting a differential fibrinolytic potential in various vascular bed [14]. Thefibrinolytic pathway ap-peared to be potentiated in the IJV as compared to SV at several levels by i) regulation of the tissue plasminogen activator gene, ii) up-regulation of the gene of the receptor for the urokinase plasminogen activator, and iii) down-regulation of the gene encoding PAI-1, the in-hibitor of the tissue plasminogen activator (tPA). Although the lower levels of PAI-1 measured in the IJV plasma might parallel the lower mRNA expression in wall, we cannot infer their direct relationship.
Higher expression of several anticoagulant and profibrinolytic genes appears to characterize the IJV wall, which would be compatible with a thromboprotective profile in respect to SV. On the other hand VWF, a key component of primary hemostasis, was highly expressed in both veins, and showed the highest level in the SV valve, a region vulnerable to thrombus formation [67]. Interestingly, the SELP gene encoding for P-selectin, found with VWF in Weibel-Palade bodies in endothelial cells, showed the highest expression levels in the SV valve as VWF. Further, several modifiers of VWF expression [50], either post-translational (ABO, ST3GAL4) or scavenger (CLEC4M, SCARA5), showed profound differences between veins, which could contribute to produce vascular specific modulation of VWF [68].
To note, data do not support differential expression of tissue factor, the main trigger of the extrinsic coagulation [69], which is highly ex-pressed in the sub-endothelium. This could imply that differences in tissue factor gene transcription do not contribute to differentiate he-mostasis balance in IJV and SV.
Upregulation in the SV wall of contact/amplification pathway genes (F11 and F12), and downregulation of prothrombinase complex (F2 and F5) and clot stabilization (F13A1) genes could participate in the local hemostasis balance with counteracting effects in the different path-ways. However, the modestly defined role of extrahepatic biosynthesis of these procoagulant factors does not help interpretation.
Noticeably, none of the hemostasis genes differentially expressed between IJV and SV walls displayed significant differences by com-paring the respective valves. Therefore, our study provides hemostasis transcriptome differences mainly concerning the wall of veins located at different sites of the vascular tree.
Among the hemostasis/venous thrombosis related genes, upregula-tion of HPSE (heparanase) and marked downregulaupregula-tion of HPSE2 (he-paranase inhibitor) in the IJV might support procoagulant features/ disease conditions, and provide an intriguing neurovascular-hemostasis bridge. As a matter of fact the heparanase inhibitor has been suggested to exert a protective role in microvascular inflammation and disease [38], and heparanase, endowed with procoagulant activity [52], has been involved in stroke, Alzheimer's disease, and likely in MS pathology [70].
The expression of LEP (leptin) and LEPR (leptin receptor) could suggest differences between IJV and SV in hemostasis-inflammation-metabolism network [71]. Binding of leptin to its receptor promotes the expression of prothrombotic and anti-fibrinolytic factors in vascular and inflammatory cells, and favors venous thrombosis in mice [53]. Intriguingly, in the SV wall we observed the lowest LEPR and the highest LEP expression levels. To balance hemostasis, IJV and SV could rely on differential expression of several coagulation inhibitors and fi-brinolysis components, and in addition of several genes involved in hemostasis-related processes.
In view of the setting of our study which includes only specimens from MS patients, the differences observed in the expression profile between IJV and SV might be also related to the disease affecting the individuals, who provided the vascular tissues. During the course of the disease cell composition of the vessel wall might change for the pre-sence of inflammatory and immunity cells. Furthermore, the gene ex-pression levels of cells or cell subpopulations might be influenced by a diseased environment, thus producing transcriptome differences be-tween IJV and SV. Additional gene expression differences could origi-nate from jugular vein malformations associated with impaired brain outflow, in MS patients under study [10,20,21]. To note, the previous comparison of the transcriptome of IJV walls between MS patients with altered brain outflow and subjects without the disease [11] did not show the differences in hemostasis gene expression that we here report. The upregulation of THBD, PROCR and PROS1 genes in IJV did not correspond to higher levels of TM, EPCR, and PS proteins in the jugular plasma. However, the TM and EPCR forms measured in plasma are shed from membranes, and thus could not properly reflect the expression of total protein. In addition, PS produced by endothelial cells is only a modest fraction of the secreted PS. Overall, many biological steps in-terfere with an affordable evaluation of the cell mRNA/protein level relationships and thus with functional interpretation. To note,
Fig. 4. Schematic representation of the expression profile of coagulation and fibrinolysis genes. Genes were selected for significant differences (fold change≥2, multiple testing correction P < 0.05) in at least one comparison. The log2expression values
Fig. 5. Hemostasis-related genes differentially expressed in microarray analysis. A) Differences between walls of internal jugular (IJV) and saphenous (SV) veins. The P-value of Benjamini-Hochberg multiple testing correction are reported (A and B).
B) Differences among all specimen groups. Mean log2expression values are shown. Error bars indicate the standard deviation. For HPSE2, CADM1, LEPR and SELP
peripheral plasma levels of TM, EPCR and PAI-1 did not differ in MS patients and healthy subjects, afinding in line with a recent observation in different healthy and MS populations [24].
Our study suffers from some limitations: i) the low number of human specimens suitable for transcriptome analysis, obtained only from MS patients, and ii) for obvious ethical reasons the lack of data concerning the expression profile of IJV specimens from healthy in-dividuals.
This exploratory and hypothesis generating study compares for the first time in human IJV and SV, both wall and valve, the transcriptome profiles modulated by vascular bed positional identity and blood flow features. Our data suggest molecular correlates of venous bed speci fi-city to be functionally investigated in relation to the vascular neuronal network and vulnerability to thrombus formation.
Supplementary data to this article can be found online athttps:// doi.org/10.1016/j.thromres.2020.04.039.
Author contributions
N.Z. set up and performed qPCR, protein analyses in plasma, sta-tistical analyses and participated in writing the manuscript; S.M. col-lected vascular specimens and performed RNA extraction and tran-scriptome analysis; E.M. collected clinical data and performed instrumental characterization of patients; M.B., B.L. and A.B. performed protein analyses in plasma; M.F. performed microarray-based tran-scriptome analysis; D.G. conducted VWF multimers assay; F.M. parti-cipated to the study design, performed instrumental characterization of patients, provided the surgical specimens and plasma samples; P.Z., F.S. recruited the patients, and performed the clinical evaluation of patients; F.B., G.M. and P.Z. conceived the study design and critically revised the manuscript; G.M. and F.B. analyzed and interpreted data, and wrote the manuscript; F.B. supervised the study. All authors approved thefinal version of the manuscript.
Funding source
The study was partially supported by the grant 2010XE5L2R_002 of the Italian Ministry of University and Research, Italy and by the grant 1786/2012 from the strategic 2010–2012 Research Program of Emilia Romagna Region, Italy.
Declaration of competing interest None.
Acknowledgements
We thank the Italian foundation“Fondazione Il Bene Onlus” for its support to multiple sclerosis research.
Disclosures None. References
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