Abstract
Vascular dementia (VaD) is a pathogenetically heterogeneous
neuropsychiatric syndrome, mainly characterized by cognitive
impairment. Among dementias, it is second by incidence after
Alzheimer’s dementia (AD). VaD biomolecular bases have been poorly
characterized, but vascular-linked factors affecting the CNS and its
functions are generally hypothesized to perform a major role, together
with cardiovascular and immunological factors. miRNAs, which perform
critically important biomolecular roles within cell networks, are also
found in biological fluids as circulating miRNAs (cmiRNAs). We
hypothesized that differentially expressed (DE) cmiRNAs in plasma from
VaD patients could be applied to diagnose VaD through liquid biopsies;
these profiles also could allow to start investigating VaD molecular
bases. By exploiting TaqMan Low-Density Arrays and single TaqMan
assays, miR-10b*, miR29a-3p, and miR-130b-3p were discovered and
validated as significantly downregulated DE cmiRNAs in VaD patients
compared to unaffected controls (NCs). These miRNAs also were found to
be significantly downregulated in a matched cohort of AD patients, but
miR-130b-3p levels were lower in AD than in VaD. A negative correlation
was detected between miR-29a and miR-130b expression and cognitive
impairment in VaD and AD, respectively. Receiver operating
characteristic curves demonstrated that decreased plasma levels of
miR-10b*, miR29a-3p, and miR-130b-3p allow to discriminate VaD and AD
patients from NCs. Furthermore, the concurrent downregulation of both
miR-10b* and miR-130b-3p in VaD showed an area under the curve (AUC) of
0.789 (p < 0.0001) with 75% of sensitivity and 72% of specificity,
whereas an AUC of 0.789 (p < 0.0001) with 92% of sensitivity and 81% of
specificity was found for both in AD. The miRNAs profiles reported in
this paper pave the way to translational applications to molecular VaD
diagnosis, but they also should allow to further investigate on its
molecular bases.
Keywords: vascular dementia, Alzheimer’s dementia, non-coding-RNAs
plasma profiles, liquid biopsies, biomarkers
Introduction
Neurodegenerative diseases (NDs) are a large group of clinically severe
and difficult to treat pathological phenotypes, which are characterized
by neuronal death in different areas of the brain (Ghavami et al.,
[45]2014). Due to an aging world population, it has been forecast that
their prevalence will dramatically increase unless strong measures are
applied to prevent their epidemic diffusion (Hebert et al., [46]2013;
Raz et al., [47]2015). Dementias are characterized by progressive
cognitive decline; they are caused by various pathological processes,
including neurodegeneration (Raz et al., [48]2015). The National Plan
to address Alzheimer’s disease classified Alzheimer’s disease-related
dementias as (1) Alzheimer’s dementia (AD), (2) vascular dementia
(VaD), (3) dementia with Lewy bodies, (4) frontotemporal dementia, and
(5) mixed dementias (Jagtap et al., [49]2015). VaD is further
classified as (a) large-vessel VaD, which includes cortical or
subcortical multi-infarct dementia and strategic infarct dementia; (b)
small vessel VaD, including subcortical ischemic dementia and other
forms of dementia due to specific arteriopathies; (c) hemorrhagic
dementia; and (d) hypoperfusion VaD (Raz et al., [50]2015). It has been
estimated that between 1 and 4% of people aged 65 years or more are
affected by VaD, whose prevalence is predicted to double every
5–10 years past the age of 65 (Raz et al., [51]2015). Currently, VaD
molecular bases have been poorly characterized (Montine et al.,
[52]2014). It then ensues that differential diagnosis with other types
of dementia as AD is very difficult to perform (Gratten et al.,
[53]2014). This represents an important hurdle for developing
presymptomatic screening tests and eventually designing personalized
therapies. It is common knowledge that most of our genome is composed
of genes encoding RNA molecules other than mRNAs: these RNAs, which do
not code for proteins, are denominated non-coding RNAs (ncRNAs) and
constitute the genome’s dark matter (Tay et al., [54]2014). ncRNAs are
classified as long non-coding RNAs (lncRNAs), if their length is
>200 nt, or small non-coding RNAs (sncRNAs), if their length is ≤200 nt
(Tay et al., [55]2014). It has been clearly demonstrated that microRNAs
(miRNAs, sncRNAs of 22–28 nt) are master regulators of networks and
pathways in critically important cellular processes (Tay et al.,
[56]2014); accordingly, miRNAs have been shown to be causally involved
in neoplastic and degenerative diseases (Geaghan and Cairns, [57]2014).
It also has been discovered that miRNAs are present in blood as
circulating miRNAs (cmiRNAs), either as cell-free complexes with
RNA-binding proteins (e.g., Ago-2) or enclosed within membrane-bound
vesicles (e.g., exosomes) (Jung and Suh, [58]2014). Since cmiRNAs are
traceable in biological fluids as serum, plasma, and cerebrospinal
fluid (CSF), it is not surprising that they have been already exploited
as molecular biomarkers for diseases affecting CNS (Geekiyanage et al.,
[59]2012). To date, this approach has not been applied to VaD: due to
the potential importance of this type of data, we sought to
characterize plasma miRNAs profiles of VaD patients and to compare them
with those from a cohort of patients affected by AD and from matched
control individuals (NCs). This allowed the identification of three
cmiRNAs (miR-10b*, miR29a-3p, and miR-130b-3p) that are significantly
downregulated in VaD, the characterization of their downstream
networks, and the identification of a set of target genes that are
involved in neurodegeneration and cardiovascular pathology.
Materials and Methods
Patient Selection
Vascular dementia and AD patients and age-, sex-, and ethnicity-matched
control individuals were recruited at Istituto Oasi Maria SS. Troina
(Enna, Italy) between January 2000 and December 2010 (Table [60]1);
plasma miRNAs profiles were analyzed at the University of Catania
between 2014 and 2015. In total, 118 individuals were selected: 38 VaD,
40 AD, and 40 NCs (Table [61]1). VaD patients were identified through
NINDS-AIREN criteria (McVeigh and Passmore, [62]2006) and AD patients
by NINCDS-ADRDA criteria (McKhann et al., [63]1984) (Tables S1 and S2
in Supplementary Material). Control individuals (NCs) were hospitalized
volunteers who did not present VaD, AD nor were affected by other
neurodegenerative, cardiovascular, and neoplastic diseases. Both
patients and NCs were Sicilian individuals of Caucasian ethnicity and
had a low-average educational attainment. Following formal written
consent, patients underwent venipuncture using dry vacutainer tubes;
blood samples were centrifuged at 4000 rpm for 15 min at 20°C to
isolate plasma, which was subdivided into aliquots and stored at −80°C
until analysis. Ethical approval for this study was provided by the
Ethics Committee of IRCCS Associazione Oasi Maria SS.
Table 1.
Demographics of VaD, AD patients, and NCs.
Samples and controls Gender Age MMSE
__________________________________________________________________
Male Female Mean SD Mean SD
VaD 18 20 82.24 6.58 14.14 5.75
AD 17 23 81.375 4.68 19 4.93
NCs 19 21 81.9 6.18 28.5 1.95
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Extraction of miRNAs from Plasma
RNA from 400 μl plasma samples was extracted by using a Qiagen miRNeasy
Mini Kit according to Qiagen Supplementary Protocol (Qiagen, GmbH,
Hilden, Germany), which allows the purification of small RNAs
(including miRNAs) from plasma or serum; RNA was eluted with 30 μl
volume of elution buffer and quantified by spectrophotometry (Rizzo et
al., [65]2015).
RNA Reverse Transcription and Preamplification: miRNAs Profiling by TaqMan
Low-Density Array
To profile plasma cmiRNAs in VaD patients, we selected and matched four
of them for sex and age with four NCs. Male/female ratio was 1; mean
age for VaD was 86.25 and for NCs 87.5; mean Mini-Mental State
Examination (MMSE) was 19 for VaD and 25 for NCs. Plasma RNAs (3 μl)
were retrotranscribed and preamplified with TaqMan MicroRNA Reverse
Transcription Kit Applied Biosystems (primers for the RT Megaplex™ RT
Primers, Human Pool A and Pool B), PreAmp Master Mix, and Megaplex
PreAmp Primers. We profiled the transcriptome of 754 miRNAs with TaqMan
Low-Density Arrays (TLDAs), TaqMan Human MicroRNA Array v3.0 A and B
(Applied Biosystems Life Technologies™, Monza, Italy), by utilizing
18 μl of preamplified products. PCRs on TLDAs were performed on a
7900HT Fast Real Time PCR System (Applied Biosystem, Life
Technologies™, Monza, Italy).
TLDAs Data Analysis
To obtain an accurate miRNAs normalization, we used the global median
normalization (GMN) method. Similar to microarray analysis, C[t] values
from each sample were normalized to the median C[t] of the array
(Ragusa et al., [66]2010). By computing Pearson correlation among C[t]
medians and means of each array and C[t] of each miRNA, we identified a
miRNA that showed an expression profile close to the median and mean of
TLDAs: miR-191-5p. We applied the statistical test significance
analysis of microarrays (SAM), included in Mev (Multi experiment viewer
v4.8.1) statistical analysis software,[67]^1 applying a two-class
paired and unpaired test among ΔC[t]s. A false discovery rate (FDR)
<0.15 was chosen as filter. Relative quantity (RQ) of miRNAs was
calculated by applying the
[MATH: 2−ΔΔCt :MATH]
method.
Single TaqMan Assays
To validate data from profiling, specific single assays were applied to
differentially expressed (DE) miRNAs among VaD, AD, and NCs, exploiting
reverse transcription (Reverse Transcription Kit, Applied Biosystem)
and Real Time PCR with TaqMan probes. miR-191-5p was used for
normalization by applying the
[MATH: 2−ΔΔCt :MATH]
method.
Statistical Analysis
All statistical analyses were performed using the MedCalc software
(Version 15.11.4). T-tests (paired and unpaired) were used to compare
miRNAs plasma levels among VaD, AD patients, and NCs. ΔC[t]s for DE
miRNAs respect to endogenous control miR-191-5p were used to generate a
receiver operating characteristic (ROC) curve. Area under the curve
(AUC) and 95% confidence intervals (95% CIs) were calculated to assess
the accuracy of each parameter (sensitivity and specificity) and to
find an appropriate cut-off point. Statistical significance was
established at a p-value ≤0.05.
Target Prediction
Validated targets of DE miRNAs were retrieved from miRTarbase, release
4.5.[68]^2 Target prediction was obtained by intersecting the
predictions by Starbase v2.0[69]^3 and DIANA-microT CDS v5.0.[70]^4
Among targets predicted by both tools, we selected those showing a
miRSVR score ≤−0.1.
Pathway Enrichment Analysis
Pathway enrichment analysis of validated and predicted targets of DE
miRNAs was performed with two different tools: Gene Trail[71]^5 and
DIANA mirPath v2.0.[72]^6 The p values for the biological categories,
obtained with the gene set analysis tool GeneTrail, were adjusted by
FDR and were considered significant if p < 0.05. The functional
annotation tool DIANA mirPath v2.0 retrieves both experimentally
verified miRNAs targets from DIANA-TarBase v7.0[73]^7 as predicted
miRNAs targets from DIANA-microT-CDS (see text footnote 3); for pathway
enrichment analysis with DIANA mirPath, we used only predicted targets
by DIANA-microT-CDS as no validated targets were found for miR-10b-3p
in DIANA-TarBase. MicroT threshold of 0.8 and p-value <0.05
(Benjamini–Hochberg correction) were selected.
Network Analysis
Selected targets and their nearest neighbors were used to construct an
interaction network with MiMi Plugin 3.1[74]^8 in Cytoscape
v2.8.3.[75]^9 Centrality analysis was performed by CentiScaPe Plugin
v.1.21,[76]^10 where parameters of betweenness, closeness, degree, and
stress were selected to identify the most central nodes. To further
analyze the biological relevance of nodes, Cytoscape plug-in ClueGO
v2.1.5 was used to perform functional enrichment analysis in Gene
Ontology and KEGG pathways.
Target mRNAs Quantification from Plasma
We extracted mRNA from 400 μl plasma by using Trizol purification
protocol; mRNAs encoding CCT5, GSK3 (targets of miR-10b*), BACE1, LPL,
NAV3 (targets of miR-29a), EDN1, ITPR1, and ZEB-1 (targets of miR-130b)
were amplified through Power Sybr Green One-Step Real Time PCR (Life
Technologies), following the manufacturer’s protocol. GPDH was used as
housekeeping gene. Primers sequences are reported in Table S3 in
Supplementary Material.
Results
Identification of DE cmiRNAs in VaD
In the discovery phase of our project, we applied TaqMan Low-Density
Array technology to profile the levels of 754 miRNAs in plasma from 4
VaD patients and 4 matched NCs. This led to the identification of 13
potentially significant DE miRNAs (Table [77]2): among these, we
focused our validation analysis on the most dysregulated miRNAs that
were endowed with qualitatively good amplification curves. In
particular, miR-886-5p and 886-3p showed apparently significant
overexpression in VaD compared to NCs, whereas miR-10b* (alternative
nomenclature: miR-10b-3p), miR-29a-3p (alternative nomenclature:
miR-29a), and miR-130b-3p (alternative nomenclature: miR-130b) showed
significant underexpression. In the validation phase of our work, we
extended our analysis to the whole cohort of 38 VaD patients and 40
NCs. Single assays for each miRNA confirmed that indeed miR-10b*,
miR-29a-3p, and miR-130b-3p are all significantly underexpressed in
plasma from VaD patients with respect to NCs (Figure [78]1).
Overexpression of miR-886-5p and miR-886-3p did not stand this further
statistical test. Expression of miR-10b*, miR-29a-3p, and miR-130b-3p
was then evaluated in plasma from 40 AD patients, matched by gender,
age, and ethnic background with the 38 VaD patients and 38 NCs
previously analyzed. This allowed us to discover that all three miRNAs
were DE in a statistically significant manner among the different
cohorts (Figure [79]1). miR-10b* was underexpressed in VaD and AD
compared to NCs, while its plasma levels were not significantly
different in the comparison between VaD and AD patients (Figure
[80]1A). A similar expression trend was observed in the analysis of
miR-29a-3p for all types of comparison (Figure [81]1B). On the other
hand, miR-130b-3p was significantly underexpressed in VaD and AD plasma
compared to NCs, but in AD patients its expression levels were lower
than in VaD (Figure [82]1C). We calculated the mathematical correlation
(i.e., Pearson and Spearman coefficients) between miRNAs expression
(ΔC[t]) and MMSE from patients and healthy controls. Through this
analysis, we found a statistically significant negative correlation
between miR-29a expression and MMSE in VaD patients (Pearson = −0.28,
p-value = 0.011; Spearman = −0.23 p-value = 0.04), and between miR-130b
expression and MMSE in AD patients (Pearson = −0.28, p-value = 0.011;
Spearman = −0.29 p-value = 0.009).
Table 2.
List of differential expressed miRNAs in VaD vs. NCs identified by
TaqMan Low-Density Arrays.
miRNAs RQ
hsa-miR-103 0.18
hsa-miR-10b* 0.35
hsa-miR-130b 0.27
hsa-miR-142-5p 0.57
hsa-miR-143 0.29
hsa-miR-145 0.77
hsa-miR-181c 0.45
hsa-miR-185 2.61
hsa-miR-223 0.48
hsa-miR-26b 0.37
hsa-miR-29a 0.22
hsa-miR-886-3p 3.47
hsa-miR-886-5p 3.04
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In bold are miRNAs selected for Single TaqMan assays. RQ, relative
quantity. False discovery rate <0.15.
Figure 1.
[84]Figure 1
[85]Open in a new tab
Box plot of miR-10b* (A), miR-29a-3p (B), miR-130b-3p (C) expression.
Validation was by single TaqMan assays in VaD patients compared to AD
and NCs, and in AD compared to NCs. Values on the y-axis are reported
as ΔC[t] × (−1). Samples analyzed: 38 VaD, 40 AD, and 40 CN.
Statistical significance was evaluated by paired T-test.
ROC Curves
By computing ROC curves, we found that the decrease of miR-10b*,
miR-29a, and miR-130b plasma levels was able to discriminate VaD and AD
patients from NCs. Specifically, we obtained for miR-10b* in VaD, an
AUC of 0.63 (95% CI, 0.507–0.759; p = 0.03) with 78% of sensitivity and
42% of specificity (ΔC[t] cut-off value: >1.73); for miR-29a in VaD, an
AUC of 0.63 (95% CI, 0.507–0.759; p = 0.03) with 76% of sensitivity and
41% of specificity (ΔC[t] cut-off value: >1.05); for miR-130b in VaD,
an AUC of 0.65 (95% CI, 0.520–0.769; p = 0.02) with 68% of sensitivity
and 53% of specificity (ΔC[t] cut-off value: >−0.62) (Figure [86]2A).
By performing the same analysis with data from AD patients, we obtained
for miR-10b*, an AUC of 0.64 (95% CI, 0.520–0.769; p = 0.02) with 74%
of sensitivity and 48% of specificity (ΔC[t] cut-off value: >2.14); for
miR-29a, an AUC of 0.64 (95% CI, 0.520–0.769; p = 0.02) with 75% of
sensitivity and 41% of specificity (ΔC[t] cut-off value: >1.05); for
miR-130b, an AUC of 0.74 (95% CI, 0.620–0.850; p > 0.0001) with 87% of
sensitivity and 61% of specificity (ΔC[t] cut-off value: >−0.43)
(Figure [87]2B). Moreover, we calculated a ROC curve also for
evaluating the discriminatory power of miR-130b in VaD patients with
respect to those with AD: an AUC of 0.65 (95% CI, 0.530–0.780;
p = 0.01) with 70% of sensitivity and 46% of specificity (ΔC[t] cut-off
value: ≤0.91) was obtained (Figure [88]2C). To improve the potential
diagnostic power of DE cmiRNAs, we also computed ROC curves for every
pair of cmiRNAs: the best score in terms of sensitivity and specificity
was obtained for the couple miR-10b*–miR-130b, which showed for VaD an
AUC of 0.789 (95% CI, 0.636–0.90; p < 0.0001) with 75% of sensitivity
and 72% of specificity (Figure [89]3A), whereas an AUC of 0.789 (95%
CI, 0.783–0.971; p < 0.0001) with 92% of sensitivity and 81% of
specificity was found for AD (Figure [90]3B).
Figure 2.
[91]Figure 2
[92]Open in a new tab
Receiver operating characteristic (ROC) curves for miR-10b*,
miR-29a-3p, and miR-130b-3p in VaD and AD patients. ROC curves of
miR-10b*, miR-29a-3p, and miR-130b-3p ΔC[t]s in VaD (A) and AD (B). (C)
ROC curve of miR-130b-3p ΔC[t]s for discrimination of VaD patients from
AD ones. Red curve represents ΔC[t]s calculated by using miR-191-5p as
endogenous control.
Figure 3.
[93]Figure 3
[94]Open in a new tab
ROC curves for couple miR-10b*–miR-130b-3p in VaD and AD patients. ROC
curves computed by considering together miR-10b* and miR-130b-3p ΔC[t]s
for detecting VaD patients (A) and AD patients (B).
Pathway Analysis
To pinpoint the biomolecular functions of these DE miRNAs, we analyzed
their mRNAs targets. This identified (A) 55 predicted targets of
miR-10b*, (B) 43 validated and 9 predicted targets of miR-29a-3p, and
(C) 7 validated targets and 14 predicted targets of miR-130b-3p (Table
S4 in Supplementary Material). By exploiting CentiScaPe Plugin, we
reconstructed a network of 2149 nodes and 23704 edges: among the 225
nodes, which were discovered to be central for all selected parameters,
14 were either validated or putative targets of DE miRNAs (Table S5 in
Supplementary Material). Through GeneTrail Pathway Enrichment Analysis,
we discovered that target genes of miR-10b*, miR-29a-3p, and
miR-130b-3p are enriched in each subcategory of eight non-neoplastic
KEGG pathways (Figure [95]4). Interestingly, the following
subcategories resulted overrepresented also by using DIANA mirPath v2.0
functional annotation tool: axon guidance, focal adhesion, neurotrophin
signaling pathway, Wnt signaling pathway (Figure [96]2). According to
ClueGO, the most involved KEGG pathways are cell cycle, focal adhesion,
FoxO signaling, hepatitis B, insulin signaling, MAPK signaling,
neurotrophin signaling, Rap1 signaling, T cell receptor signaling;
three of these (focal adhesion, MAPK signaling, and neurotrophin
signaling) were also identified through GeneTrail. Among the mRNAs
targets of miR-10b*, miR-29a-3p, and miR-130b-3p, which were analyzed
in plasma from VaD and AD patients and NCs (i.e., BACE1, CCT5, EDN1,
GSK3B, ITPR1, LPL, NAV3, and ZEB1), only ZEB1 (target of miR-130b-3p)
was detected; however, its plasma levels were not different among the
individuals of the three cohorts analyzed.
Figure 4.
[97]Figure 4
[98]Open in a new tab
Statistical overrepresented KEGG pathways, identified by GeneTrail, and
target genes included in each KEGG category.
Discussion
miRNAs as VaD Molecular Biomarkers
The important biomolecular roles played by miRNAs within organisms at
all levels of the evolutionary scale have been demonstrated (Zheng et
al., [99]2016). Many miRNAs are enriched in specific organs, tissues,
and cell types, for instance, in different areas of the brain or in
specific subcompartments of neurons (e.g., axons, dendrites, and
synapses) (He et al., [100]2012). cmiRNAs have been detected in plasma,
serum, whole blood, urine, saliva, sweat, breath, and cerebrospinal
fluid (Li and Zhang, [101]2015). Because of their small molecular size
compared to proteins, cmiRNAs cross biological barriers (e.g.,
blood/brain, blood/placenta); they also are present within exosomes
isolated from body fluids (Li and Zhang, [102]2015). cmiRNAs have been
already exploited as biomarkers for depression, bipolar disorder,
schizophrenia, and Alzheimer’s disease (Ha, [103]2011), but not for
VaD. VaD is thought to be caused by diminished cerebral blood flow
leading to hypoxia and blood–brain barrier altered permeability:
vasculotoxic and neurotoxic effects ensue, which may promote
neurodegeneration. VaD may be caused by different pathological events
as stroke, cerebral hemorrhage, traumas, chronic diseases as
atherosclerosis, large and small vessel disease, and cardioembolic
disease (Iadecola, [104]2013). It is frequently associated with
diabetes, hypertension, hypercholesterolemia, and smoking. In contrast
to AD, VaD genetic bases are not well defined (Montine et al.,
[105]2014). Due to the high compensatory potential of the brain, both
AD and VaD are characterized by late clinical manifestations (Raz et
al., [106]2015). This delay clearly calls for early activation of
diagnostic presymptomatic and preventive procedures: when the disease
becomes clinically evident, pharmacological intervention may no longer
be very effective (Hebert et al., [107]2013; Raz et al., [108]2015).
Diagnostic criteria for AD and VaD, based on clinical evaluation of
cognitive decline and deterioration of functional abilities, have been
proposed by (1) the Diagnostic and Statistical Manual of Mental
Disorders, Fifth Edition (DSM-5) (Regier et al., [109]2009), (2) the
International Classification of Diseases (ICD-10), (3) the USA National
Institute of Neurological Disorders and Stroke and the Association
Internationale pour la Recherche et l’Enseignement en Neurosciences
(NINDS-AIREN) (McVeigh and Passmore, [110]2006), (4) the MMSE (Folstein
et al., [111]1975), and (5) the California Alzheimer’s Disease
Diagnostic and Treatment Centres (CAD-DTC) (Chui et al., [112]1992).
Moreover, the most recent applications of magnetic resonance imaging
(MRI) and computed tomography (CT) make possible to analyze in detail
the brain structure and confirm the diagnosis of cerebrovascular
diseases as VaD (Van Straaten and Stam, [113]2013). We propose that the
molecular data reported in this paper nicely complement the diagnostic
approaches synthesized above: in fact, our data show that miR-10b*,
miR-29a-3p, and miR-130b-3p are DE in plasma from VaD patients with
respect to NCs. They also are underexpressed in plasma from AD patients
with respect to NCs, but the levels of miR-130b-3p are lower in AD than
in VaD patients (Figure [114]1B). A negative correlation exists between
miR-29a ΔC[t] and MMSE in VaD, as between miR-130b ΔC[t] and MMSE in
AD. These data showed that plasma levels of these two miRNAs decreased
as the cognitive impairment increased, suggesting a hypothetical link.
ROC curve analysis suggests that these miRNAs could be considered
useful markers to diagnose VaD and AD. miR-130b levels were also able
to discriminate VaD from AD with 70% of sensitivity and 46% of
specificity. Intriguingly, by considering the diagnostic efficiency of
different pairs of cmiRNAs, we found that the concurrent downregulation
of both miR-10b* and miR-130b-3p improved their discriminatory power of
VaD and AD patients. Accordingly, miR-10b*, miR-29a-3p, and miR-130b-3p
are all to be considered good markers of VaD and AD (and of
neurodegeneration in general), but miR-130b-3p may be specifically
exploited as a differential diagnostic marker between VaD and AD. We
found in literature no previous mention of circulating or cellular
miR-10b* related to pathological or physiological conditions. On the
other hand, decreased levels of miR-29a in serum and whole blood of AD
patients were previously reported (Geekiyanage et al., [115]2012;
Leidinger et al., [116]2013). Recently, a meta-analysis of cmiRNAs
deregulated in AD and MCI from 18 independent published reports was
published: the downregulation of miR-29a in serum or plasma from AD
patients was reported by four independent papers, and in two of them
its deregulation resulted statistically significant (Wu et al.,
[117]2015). The authors concluded that the inconsistence of results
across different studies should be the consequence of multiple
biological and technical factors. miRNAs expression could be influenced
by age, genetic variations related to ethnicity, environmental factors,
comorbid conditions, and by biological source of miRNAs (e.g., serum or
plasma), sample preparation, analysis platform, normalization method,
and statistical approaches. Notably, about 40% of papers reporting data
on cmiRNAs in AD and MCI were studies on Chinese population (Wu et al.,
[118]2015). An altered expression of miR-130b was reported in
oxidatively stressed primary hippocampal neurons and different strains
of senescence-accelerated mice, suggesting its potential role in the
pathogenesis of NDs (Zhang et al., [119]2014). Xie et al. ([120]2015)
assayed levels of miR-130b and other AD-related miRNAs in serum of
patients with mild cognitive impairment (MCI); based on their results,
miR-130b was not deregulated in MCI patients with respect to normal
controls.
Circulating miRNAs in VaD, Their Downstream mRNA Targets, and Corresponding
Gene Network
We hypothesized that DE cmiRNAs detected in our experiments could shed
a light on the genes and pathways involved in VaD, which remain still
elusive. Indeed, our computational analysis on validated and predicted
miRNA targets allowed us to discover that miR-10b*, miR-29 family, and
miR-130b-3p perform important functions in regulating CNS activity and
also are involved in neurodegenerative and cardiovascular diseases: it
is worth to stress that both CNS and cardiovascular system are involved
in VaD. Among miR-10b* targets, Glycogen synthase kinase3-β (GSK3β) is
involved in mechanisms underlying learning and memory. It also is
involved in local responses to cerebral inflammatory processes
(Llorens-Martín et al., [121]2014). Moreover, GSK3β overexpression
inhibits brain-derived neurotrophic factor (BDNF)-mediated survival
pathway (Liu et al., [122]2015). GSK3β is comprised in six and four of
the pathways listed by the DIANA mirPath and GeneTrail, respectively
(Figure [123]3). The miR-29 family comprises three members (i.e.,
miR-29a, miR-29b, and miR-29c) that have important roles in both
nervous and cardiovascular systems (Kriegel et al., [124]2012). A
significant negative correlation was detected between miR-29a-3p and
Aβ42 peptide in CSF and blood from AD patients (Hébert et al.,
[125]2008). Expression of miR-29a/b-1 cluster was found to be
significantly decreased in AD patients, coupled to abnormally high
levels of BACE1 protein (Hébert et al., [126]2008). Similar
correlations between expression of this miRNA cluster and BACE1 were
found during brain development and in primary neuronal cultures (Hébert
et al., [127]2008). Notably, our data showed significant negative
correlation between miR-29a expression and cognitive impairment in VaD
patients. Neuron navigator 3 (NAV3), involved in axon guidance, is a
very important target of miR-29a-3p: underexpression of miR-29a-3p
affects neurodegenerative processes by enhancing neuronal NAV3
expression in AD brains (Shioya et al., [128]2010). The potentially
important pathogenetic role of miR-130b-3p in VaD is suggested by the
biomolecular functions of its targets. EDN1 is the most powerful among
the three members of the endothelin family (EDN1, EDN2, and EDN3)
(Maguire and Davenport, [129]2014). They are synthesized mainly in the
endothelium and perform a key role in the homeostasis of the vascular
system, acting as powerful vasoconstrictor agents (Agapitov and Haynes,
[130]2002). Endothelins are primarily involved in cerebral circulation
deficits and in the pathogenesis of many heart and circulatory system
diseases (Agapitov and Haynes, [131]2002). Another relevant target of
miR-130b-3p is ENPP5: it is highly expressed in the brain and plays an
important role in communication among neuronal cells (Ohe et al.,
[132]2003). Its overexpression is important after the onset of neuronal
damage, when CNS attempts to reestablish lost neuronal connections
(Schinelli, [133]2006). Both EDN1 and ENPP5 functions would agree with
potential involvement of miR-130b-3p underexpression in contributing to
neurodegeneration. It is worth to stress that miR-130b-3p,
downregulated in both VaD and AD with respect to NCs, was able to
discriminate the two diseases with 70% of sensitivity and 46% of
specificity, and its expression was negatively correlated with
intellectual deterioration in AD. In Figure [134]5, the potential
cellular pathways controlled by miR-10b*, miR-29a-3p, and 130b-3p are
depicted, based on their interactions with targets reported above.
Figure 5.
[135]Figure 5
[136]Open in a new tab
Cellular pathways controlled by miR-10b* (A), miR-29a (B), and 130b-3p
(C). APP, amyloid precursor protein; ECE, endothelin converting enzyme;
p+, phosphorylation.
Conclusion
Our study describes a set of non-invasive miRNA biomarkers, which are
detectable in plasma of VaD patients, and could confer molecular
precision and improve detection power of VaD diagnosis. The use of
cmiRNAs as biomarkers should be effectively associated with other
traditional well validated markers of VaD (e.g., structural and
molecular imaging). Further multicentric studies on larger cohorts of
patients and on other NDs will be needed to verify the effective
diagnostic and discriminatory power of miR-10b*, miR-29a-3p, and
130b-3p and other cmiRNAs in VaD and AD. Profiling of circulating
ncRNAs in NDs could have an important impact on clinical practice in
Neuropsychiatry and also be exploited to improve our understanding of
still partially characterized molecular aspects of these phenotypes
(Geekiyanage et al., [137]2012; Geaghan and Cairns, [138]2014).
Author Contributions
MP conceived and coordinated the project with the critical
collaboration of MR. MP, MR, PB, ME, MS, and CP designed experiments;
LT, MT, and DB performed them. RS carried out patient’s recruitment and
clinical data analysis. CB and AC performed computational analysis. LT
realized statistical analysis. MP, MR, and LT wrote the paper. All
authors contributed to the critical revision of the data, read, and
approved the final manuscript.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Acknowledgments