Abstract
Background
Recent research has investigated the connection between Diabetes
Mellitus (DM) and Alzheimer’s Disease (AD). Insulin resistance plays a
crucial role in this interaction. Studies have focused on dysregulated
proteins to disrupt this connection. Non-coding RNAs (ncRNAs), on the
other hand, play an important role in the development of many diseases.
They encode the majority of the human genome and regulate gene
expression through a variety of mechanisms. Consequently, identifying
significant ncRNAs and utilizing them as biomarkers could facilitate
the early detection of this cross-talk. On the other hand,
computational-based methods may help to understand the possible
relationships between different molecules and conduct future wet
laboratory experiments.
Materials and methods
In this study, we retrieved Genome-Wide Association Study ([31]GWAS,
2008) results from the United Kingdom Biobank database using the
keywords “Alzheimer’s” and “Diabetes Mellitus.” After excluding low
confidence variants, statistical analysis was performed, and adjusted
p-values were determined. Using the Linkage Disequilibrium method, 127
significant shared Single Nucleotide Polymorphism (SNP) were chosen and
the SNP-SNP interaction network was built. From this network, dense
subgraphs were extracted as signatures. By mapping each signature to
the reference genome, genes associated with the selected SNPs were
retrieved. Then, protein-microRNA (miRNA) and miRNA-long non-coding RNA
(lncRNA) bipartite networks were built and significant ncRNAs were
extracted. After the validation process, by applying the scoring
function, the final protein-miRNA-lncRNA tripartite network was
constructed, and significant miRNAs and lncRNAs were identified.
Results
Hsa-miR-199a-5p, hsa-miR-199b-5p, hsa-miR-423-5p, and hsa-miR-3184-5p,
the four most significant miRNAs, as well as NEAT1, XIST, and KCNQ1OT1,
the three most important lncRNAs, and their interacting proteins in the
final tripartite network, have been proposed as new candidate
biomarkers in the cross-talk between DM and AD. The literature review
also validates the obtained ncRNAs. In addition, miRNA/lncRNA pairs;
hsa-miR-124-3p/KCNQ1OT1, hsa-miR-124-3p/NEAT1, and hsa-miR-124-3p/XIST,
all expressed in the brain, and their interacting proteins in our final
network are suggested for future research investigation.
Conclusion
This study identified 127 shared SNPs, 7 proteins, 15 miRNAs, and 11
lncRNAs involved in the cross-talk between DM and AD. Different network
analysis and scoring function suggested the most significant miRNAs and
lncRNAs as potential candidate biomarkers for wet laboratory
experiments. Considering these candidate biomarkers may help in the
early detection of DM and AD co-occurrence.
Keywords: biomarker detection, Alzheimer’s disease (AD), diabetes
mellitus (DM), type 3 diabetes mellitus, brain diabetes, genome wide
association study, non-coding RNA, bioinformatics
Introduction
Diabetes Mellitus (DM) and Alzheimer’s Disease (AD) are two of the most
prevalent diseases in the elderly population of the world. DM cases are
increasing exponentially, and by 2035 there will likely be 592 million
cases. Moreover, the incidence of DM-induced AD in the human population
has increased rapidly over the past decade. DM not only increases the
risk of AD, but also doubles or triples the development rate of it.
Given the apparent relationship between these two diseases, it has been
suggested that AD is actually a neuroendocrine disorder and not just a
neurologic one. Similar to insulin resistance in the brain, AD brains
have decreased glucose uptake from the environment. Patients with DM
and AD have amyloid beta deposits in the pancreas and brain. In
addition, certain DM target receptors are involved in the regulation of
tau expression and phosphorylation, a significant target protein in AD.
This phenomenon is referred to as the cross-talk between DM and AD
([32]Kandimalla et al., 2017).
Diabetes Mellitus and its complications are primarily caused by insulin
resistance and impaired insulin signaling. Insulin encourages cells to
absorb glucose. It is also required for normal cognitive function of
hippocampus. Several studies have found that brain insulin resistance
is a key factor in the link between DM and AD ([33]Baglietto-Vargas et
al., 2016; [34]Spinelli et al., 2019; [35]McNay and Pearson-Leary,
2020). There are additional cellular and molecular reasons besides
impaired insulin signaling. The majority of the main evidence includes
oxidative-stress, inflammation, obesity, dysregulation of
Apolipoprotein E (APOE), insulin degradation enzyme (IDE), Glucose
Transporter type 4 (GLUT4), and Acetylcholinesterase (AChE), are linked
to insulin resistance ([36]Mittal and Katare, 2016).
[37]Mittal et al. (2016) derived differentially expressed proteins of
Type 2 Diabetes Mellitus (T2DM) and AD individually 70 proteins in
total and retrieved the first neighbor of each protein in
protein-protein interaction (PPI) network (957 proteins). Using pathway
analysis and interactions between these proteins, the most relevant
proteins for both T2DM and AD were identified ([38]Mittal et al.,
2016). [39]Karki et al. (2017) used Biological Expression Language
(BEL) to identify six significant shared pathways between T2DM and AD
at both the molecular and clinical levels. They also discovered that
some drugs used to treat T2DM might increase the risk of AD ([40]Karki
et al., 2017). Another research ([41]Shakil, 2017) demonstrated that
antidiabetic drugs can be used to treat Alzheimer’s disease. Based on
this hypothesis, molecular docking was used to study two antidiabetic
drugs, ertugliflozin and sotagliflozin, and their targets, sodium
glucose cotransporter 2 and AChE, respectively. Finally, sotagliflozin
structure was proposed for designing drugs that may treat DM and AD
simultaneously. [42]Hu et al. (2020) retrieved data from the ROSMAP
Project for T2DM and AD, analyzed multi-omic data using an inference
method and identified common pathways between these two diseases.
Furthermore, variations in the human genome may increase the risk of
chronic diseases like DM and AD. As a result, Genome-Wide Association
Study (GWAS) projects were conducted to identify new loci linked to
both diseases. [43]Hao et al. (2015) investigated shared genetic risk
factors in the association between T2DM and AD using a systems biology
approach. They discovered genes involved in both diseases using GWAS
analysis. By combining gene ontologies, co-expression networks, and
regulatory elements, they discovered shared Single Nucleotide
Polymorphisms (SNPs) and their associated genes as novel targets for
further investigation. In order to identify the most important pathways
in the cross-talk between T2DM and AD, they finally used pathway
enrichment analysis.
Despite the importance of protein dysregulation in the development of
many diseases, recent research has revealed that non-coding RNAs
(ncRNAs) also play a significant role in the progression of complex
diseases ([44]Li and Kowdley, 2012; [45]Natarajan et al., 2013;
[46]DiStefano, 2018; [47]Lekka and Hall, 2018). Furthermore, the
majority of the human genome encodes ncRNAs, with only a small
percentage converting to proteins. Therefore, researchers believe that
ncRNAs have a significant impact on neurodegeneration processes that
lead to AD ([48]Idda et al., 2018) and metabolic disorders that lead to
DM ([49]Alvarez and Distefano, 2013). Approximately 70% of
experimentally detected microRNAs (miRNAs) are expressed in the brain
and are involved in memory and synapses formation ([50]Silvestro et
al., 2019). Additionally, they regulate the function of insulin and,
consequently, their dysregulation has been linked to the progression of
DM ([51]Nigi et al., 2018). On the other hand, long non-coding RNAs
(lncRNAs), also have been linked to insulin resistance, inflammation,
and DM ([52]Sathishkumar et al., 2018), as well as the progression of
AD ([53]Luo and Chen, 2016).
Despite producing some promising and valuable insights, less attention
has been paid to the impact of ncRNAs on the cross-talk between DM and
AD to date. Therefore, this research is motivated by the fact that,
while dysregulated proteins play an important role in the progression
of many diseases, ncRNAs also have a strong impact on complex diseases.
Consequently, using GWAS results and statistical and network analysis,
we proposed candidate miRNAs and lncRNAs as potential biomarkers for
early detection of the co-occurrence of DM and AD. The following covers
data collection, networks analysis, validations, and scoring function
in the section “Materials and methods.” Significant ncRNAs in the final
tripartite network were introduced in the section “Result.” We believe
that the paths in this network from lncRNAs to proteins (which pass
through miRNAs) recommend potential candidate biomarkers that may also
be drug targets in the cross-talk between DM and AD. We will discuss
them in detail in the section “Discussion.”
Materials and methods
Data set
We retrieved GWAS results using keywords “Alzheimer’s” and “Diabetes”
from Neale Lab^[54]1, which performed GWAS analysis from the United
Kingdom Biobank database ([55]Bycroft et al., 2018). For DM and AD 35
and 11 files were downloaded in both sexes, respectively. GWAS results
in each file included genotyped and imputed data on 13,791,467 SNPs
from more than 300,000 volunteers in both diseases. The beta
coefficient, p-value, minor allele frequency, and standard error were
all included in each dataset. To find protein-miRNA and miRNA-lncRNA
interactions, we used the [56]Starbase (2010) database ([57]Li et al.,
2014).
Mapping single nucleotide polymorphisms to proteins
Initially, significant SNPs from each disease should be chosen. We
employed two statistical parameters: Minor Allele Frequency (MAF) and
adj.p-value. MAF is a standard used to determine the frequency of the
second most common allele for a particular SNP. Due to the fact that
variants with low confidence (MAF < 0.001) did not meet the MAF
threshold, they were removed from the DM and AD datasets. The
adj.p-value for the remaining SNPs was calculated using the FDR method
in R. Significant SNPs were determined to have adj.p-value < 0.05.
Following data cleaning, significant SNPs revealed by both diseases
were selected.
LD is a commonly used method for calculating SNP correlation. It was
employed to identify interactions between SNPs. It is only applicable
to SNPs located on the same chromosome. It generates two disequilibrium
values; D’ and R^2, describing how frequently alleles are inherited
together. LD performed a chi-squared test for each pair of SNPs and
calculates D’ (S[i], S[j]) and R^2 (S[i], S[j]) and reports the p-value
(S[i], S[j]) ([58]Slatkin, 2008). We calculated LD using the NIH API in
R (National Cancer Institute^[59]2). Then, we created the graph G[s]
which its nodes represent the significant SNPs that were identified in
the preceding step. We considered an edge between two SNPs if D’ (S[i],
S[j]) >0.7 and p-value (S[i], S[j]) < 0.0001. This graph contains of
four connected components. Two components correspond to SNPs on
chromosome 19, and the other two components correspond to SNPs on
chromosome 6. For each component, we looked for dense subgraphs that
contained at least 20% of the nodes in that component. Then, the nodes
of each dense subgraph are regarded as a collection of SNP’s
signatures.
To identify genes (proteins and ncRNAs) associated with the signatures,
we utilized the UCSC Genome Browser^[60]3 and retrieved information
about the genes and their biological consequences. SNPnexus^[61]4 was
then used to annotate the achieved genes and their corresponding
proteins. Next, the [62]Disgenet database (2014), a database for
disease gene associations, was used to determine if these proteins are
linked to both DM and AD.
Protein-miRNA and miRNA-long non-coding RNA networks
Assume that p[1], p[2], …, p[n] are proteins achieved from the previous
step and m[1], m[2], …, m[l] are miRNAs, obtained from the starbase
database, that interacts with at least one of these proteins. The
protein-miRNA bipartite network was subsequently created. The nodes of
one part are the set P = {p[1], p[2], …, p[n]} and the other part are
the set M = {m[1], m[2], …, m[l]}. If miRNA m[j] regulates protein
p[i], there is an edge between p[i] and m[j]. The top 10 miRNAs with
the highest degree in the corresponding network were selected as
significant, formally denoted by M’ = {m’[1], m’[2], m’[3],., m’[s]}.
Next, suppose that l[1], l[2], …, l[r] are lncRNAs, retrieved from the
starbase database, that interact with at least one of the miRNAs m’[i].
Similarly, the miRNA-lncRNA bipartite network was constructed. The
nodes of one part is the set M’ and the other part is the set L =
{l[1], l[2], …, l[r]}. There is an edge between m’[i] and l[j] if
lncRNA l[j] regulates miRNA m’[i.] In the constructed network,
similarly to previous network, the top 10 lncRNAs with the highest
degree were chosen as significant, formally denoted by L ‘ = {l’[1],
l’[2], l’[3],., l’[t]}.
Protein-miRNA-long non-coding RNA tripartite network
After passing validation, the final protein-miRNA-lncRNA tripartite
network was constructed. The first layer of this network is represented
by the set P, the second by the set M’, and the third by the set L’.
Similar to the previous bipartite networks, if miRNA m’[j] regulates
protein p[i], there is an edge between p[i] and m’[j]. In addition,
there is an edge between m’[i] and l’[j] if lncRNA l’[j] regulates the
miRNA m’[i.] To validate the edges of this network we considered the
following well-known databases. We used [63]TargetScan (2005),
[64]miRwalk (2011), [65]microT (2012), and [66]miRmap (2012) databases
to validate protein-miRNA interactions and [67]LncBase (2012) database
to validate miRNA-lncRNA interactions. If an edge was reported in at
least one of the aforementioned databases, the interaction was kept in
the final network; otherwise, it was removed.
Scoring function
By assigning a score to ncRNAs, the significance of the nodes in the
tripartite network was determined. Assume that N[P] is the total number
of proteins in the final tripartite network and
[MATH: NP(m)′i :MATH]
denotes the set of proteins regulated by miRNA
[MATH:
m′i :MATH]
. The scoring function was calculated as follows:
[MATH:
S(m
′i)=NP(m)′iN
P :MATH]
(1)
[MATH: S(l′i)=∑m′j
ϵNm′(l′i))S
(m′j)
:MATH]
(2)
Where
[MATH:
Nm′(l′i) :MATH]
denote the set of miRNAs which regulated by lncRNA
[MATH:
l′i :MATH]
.
In fact, to calculate
[MATH: S(l′i) :MATH]
, all the miRNAs in the tripartite network, regulated by lncRNA
[MATH:
l′i :MATH]
were retrieved and the sum of the scores of the corresponding miRNAs
was assigned as the score of the intended lncRNA.
The research workflow is depicted in [68]Figure 1.
FIGURE 1.
[69]FIGURE 1
[70]Open in a new tab
The workflow of the study.
Results
Networks analysis
There were 127 common SNPs among the 1470 and 240251 significant SNPs
identified by analyzing AD and DM GWAS results, respectively.
Forty-three SNPs occurred on chromosome 19 and 84 SNPs on chromosome 6.
[71]Supplementary Table 1 contains all the information about common
SNPs revealed by both diseases. To construct the SNP-SNP interaction
network LD was calculated and different D’ value thresholds were
tested. The final network consists of four components. Two components
are associated to chromosome 19, while the remaining two are linked to
chromosome 6. As we have already mentioned, to identify signatures, we
considered dense subgraphs containing at least 20% of the chromosome’s
total nodes. As a result, the subgraphs linked to chromosomes 19 should
contain at least 8 and subgraphs linked to chromosomes 6 should contain
at least 16 nodes. Each of the 43 SNPs on chromosome 19 belongs to at
least one signature. Ten of the 84 SNPs on chromosome 6 do not
participate in any signature; some are members of small dense subgraphs
(which do not meet the signature threshold), while others do not form
any dense subgraph. The results are shown in [72]Table 1 and [73]Figure
2.
TABLE 1.
Common significant SNPs between DM and AD.
1 rs112588918 21 rs157581 41 rs157592 61 rs112077259 81 rs35743245 101
rs34331363
2 rs2199575 22 rs34095326 42 rs111789331 62 rs2894188 82 rs36096565 102
rs35226637
3 rs57537848 23 rs34404554 43 rs66626994 63 rs3095242 83 rs35972518 103
rs35653258
4 rs11666329 24 rs11556505 44 rs576224725 64 rs3095241 84 rs35917796
104 rs2647059
5 rs2972559 25 rs157582 45 rs3095250 65 rs3130413 85 rs35380574 105
rs2647062
6 rs71338733 26 rs59007384 46 rs3132496 66 rs2394906 86 rs35395738 106
rs558721
7 rs73050205 27 rs769449 47 rs3095248 67 rs3130416 87 rs35472547 107
rs679242
8 rs199956232 28 rs429358 48 rs3130712 68 rs3130418 88 rs34939562 108
rs2760990
9 rs4803763 29 rs75627662 49 rs3130406 69 rs3134750 89 rs34924558 109
rs2647066
10 rs4803764 30 rs10414043 50 rs3130535 70 rs3130431 90 rs34496598 110
rs17425622
11 rs142042446 31 rs7256200 51 rs3130688 71 rs9264187 91 rs35525122 111
rs601148
12 rs12972156 32 rs483082 52 rs3134766 72 rs7769393 92 rs34350244 112
rs601945
13 rs12972970 33 rs438811 53 rs3130536 73 rs4458721 93 rs34535888 113
rs3130411
14 rs34342646 34 rs34954997 54 rs3134764 74 rs35899943 94 rs34553045
114 rs9271494
15 rs283811 35 rs5117 55 rs3134763 75 rs1980496 95 rs2760980 115
rs6917729
16 rs283815 36 rs12721046 56 rs2394900 76 rs9268433 96 rs2760983 116
rs6605556
17 rs6857 37 rs12721051 57 rs34763471 77 rs3793127 97 rs113134061 117
rs9268455
18 rs71352238 38 rs56131196 58 rs2394901 78 rs3763309 98 rs2760984
19 rs184017 39 rs4420638 59 rs3095244 79 rs3763312 99 rs2454139
20 rs2075650 40 rs814573 60 rs3134757 80 rs9269041 100 rs34117221
[74]Open in a new tab
The SNPs represented in this table are depicted in [75]Figure 2.
FIGURE 2.
[76]FIGURE 2
[77]Open in a new tab
Single Nucleotide Polymorphism (SNP)-SNP interaction network. This
network consists of four components. Component 1 consists of signature
(A) (light blue nodes) and component 2 consists of signature (B) (red
nodes). Component 3 consists of signature (C) (green nodes) and
component 4 consist of signatures (D–F). The dark blue nodes in the
last component are shared by signatures (D–F) while the yellow, purple,
and light pink nodes are unique to signatures (D–F), respectively. The
first two components are found on chromosome 19, while the last two are
found on chromosome 6. Each SNP rsid number is listed in [78]Table 1.
After mapping each signature to the reference genome, 7 proteins, 2
lncRNAs, and one pseudogene were identified. Genes with at least 3 SNPs
were reported in our dataset. According to [79]Figure 3, HLA-DRB1and
APOE have the highest and lowest number of SNPs, respectively.
Signatures and their associated genes are displayed in [80]Table 2.
FIGURE 3.
[81]FIGURE 3
[82]Open in a new tab
Distribution of common significant SNPs on the associated genes. APOE
(chromosome 19) and HLA-DRB1 (chromosome 6) have the lowest and the
highest number of SNPs.
TABLE 2.
Mapping signatures to the reference genome.
Gene Annotation Chr No. Signature ID
NECTIN2 Protein coding 19 A
[83]AC011481.2 lncRNA 19 B
TOMM40 Protein coding 19 B
APOE Protein coding 19 B
APOC1 Protein coding 19 B
[84]AL662844.2 Unprocessed pseudogene 6 C
HLA-C Protein coding 6 C
HLA-DRB1 Protein coding 6 D, E
HLA-DQA1 Protein coding 6 D, E, F
TSBP1-AS1 lncRNA 6 D, F
[85]Open in a new tab
This table contains the annotation of the obtained genes, the
chromosome number, and the corresponding signature. Some SNPs are found
in multiple signatures.
To highlight the importance of miRNAs in the progression of both
diseases we created a protein-miRNA bipartite network. This network
showed that among 238 miRNAs, hsa-miR-199a-5p, hsa-miR-199b-5p,
hsa-miR-423-5p, hsa-miR-3184-5p, hsa-miR-124-3p, hsa-miR-506-3p,
hsa-miR-1321, hsa-miR-4731, hsa-miR-491-5p, hsa-miR-663a,
hsa-miR-744-5p, hsa-miR-665, hsa-miR-1286, hsa-miR-1908-5p, and
hsa-miR-873-5p are the top 10 high-degree miRNAs in the network. Since
some nodes shared the same degree, 15 miRNAs were chosen.
Next, miRNA-lncRNA bipartite network was built and high degree lncRNAs
were chosen. Among 350 lncRNAs in this network, Nuclear paraspeckle
assembly transcript 1 (NEAT1), [86]AC069281.2, X inactive specific
transcript (XIST), KCNQ1 opposite strand/antisense transcript 1
(KCNQ1OT1), [87]AC010442.1, [88]AC092127.1, SLC9A3-AS1, KRTAP5-AS1,
[89]AC010327.5, STAG3L5P-PVRIG2P-PILRB, and LINC00963 are the top 10
high-degree ones. Since some nodes shared the same degree, 11 lncRNAs
were chosen. [90]Supplementary Tables 2, [91]3 contain all the
protein-miRNA and miRNA-lncRNA interactions.
Validations
To ensure that the achieved proteins are associated with the mentioned
diseases, we used the Disgenet database. The findings revealed that
APOE is a key gene in the progression of both diseases. APOC1, TOMM40,
and NECTIN2 play a bigger role in AD than in DM, while HLA-C, HLA-DQA1,
and HLA-DRB1 play a bigger role in DM than in AD. Nevertheless, all
proteins are involved in both diseases. Results have been shown in
[92]Table 3.
TABLE 3.
Protein validation.
Protein Disease N_PMIDs
APOE Alzheimer’s disease 3042
Alzheimer’s disease, late onset 431
Diabetes mellitus 87
Diabetes mellitus, non-insulin-dependent 83
Diabetes mellitus, insulin-dependent 14
APOC1 Alzheimer’s disease 20
Alzheimer’s disease, late onset 5
Diabetic nephropathy 1
TOMM40 Alzheimer’s disease 92
Alzheimer’s disease, late onset 24
Diabetes mellitus, non-insulin-dependent 4
NECTIN2 Alzheimer’s disease 25
Alzheimer’s disease, late onset 6
Diabetes mellitus, non-insulin-dependent 1
HLA-C Alzheimer’s disease 2
Diabetes mellitus, insulin-dependent 24
HLA-DQA1 Alzheimer’s disease 1
Diabetes mellitus, insulin-dependent 191
Diabetes mellitus, non-insulin-dependent 3
HLA-DRB1 Alzheimer’s disease 10
Alzheimer’s disease, late onset 1
Diabetes mellitus, insulin-dependent 279
Diabetes mellitus, non-insulin-dependent 17
[93]Open in a new tab
To increase confidence to the final network, all interactions were
double-checked against TargetScan miRwalk, miRmap, microT and LncBase
databases. As shown in [94]Table 4, all protein-miRNA interactions
passed the validation process and no edge has been removed. However,
according to [95]Table 5, some edges did not satisfy the validation
process for miRNA-lncRNA interactions and were removed.
TABLE 4.
Protein-miRNA interaction validation.
APOC1 APOE HLA-C HLA-DQA1 HLA-DRB1 TOMM40 NECTIN2
miR-199a-5p a, b 0 0 a, b 0 a, b, c a, b
miR-199b-5p a, d 0 0 a, f 0 a, b, c a, b
miR-423-5p 0 0 a, b a, b 0 a, b a, b
miR-3184-5p 0 0 a, b a, b 0 a, b a, b
miR-124-3p a, d 0 0 a, b 0 a, e 0
miR-506-3p a, b 0 0 a, b 0 a, b 0
miR-1321 a, b 0 0 0 a, b, c a, b 0
miR-4731-5p a, b, c 0 0 0 0 a, b a, b
miR-491-5p 0 a, e a, b, c 0 0 a, b 0
miR-663a 0 a, b, c, e 0 0 0 a, b a, b
miR-744-5p 0 a, b 0 0 0 a, b a, b
miR-665 0 a, b, c 0 0 0 a, b a, b
miR-1286 0 a, e 0 0 a, b a, e 0
miR-1908-5p 0 a, b, c, e 0 0 0 a, b a, b
miR-873-5p 0 0 a, c 0 0 a, b a, c
[96]Open in a new tab
In this table, all protein-miRNA interactions were validated with other
databases. In the table, a: Starbase database, b: miRwalk database, c:
TargetScan database, d: miRanda database (accessed from starbase
database), e: miRmap database, f: microT database. “0” denotes there is
no interaction between intended protein and miRNA. Edges that passed
the validation process has been bolded.
TABLE 5.
MiRNA-lncRNA interaction validation.
NEAT1 [97]AC069281.2 XIST KCNQ1OT1 [98]AC010442.1 [99]AC092127.1
SLC9A3-AS1 KRTAP5-AS1 [100]AC010327.5 STAG3L5P LINC00963
miR-124-3p S, L S S, L S, L S, L 0 0 0 S 0 0
miR-1286 S S 0 0 0 S 0 0 S 0 0
miR-1321 S, L S, L S, L S, L S S S S 0 S S
miR-1908-5p S S S 0 0 S S S 0 0 S
miR-199a-5p 0 0 0 S, L 0 0 0 S, L 0 S S
miR-199b-5p 0 0 0 S, L 0 0 0 S, L 0 S S
miR-3184-5p S, L S S, L S, L S S, L S S S S 0
miR-423-5p S, L S S, L S, L S, L S, L S, L S, L S, L S, L 0
miR-4731-5p S, L S, L S, L S, L 0 S S S S 0 S, L
miR-491-5p S, L S S, L S, L S, L 0 0 0 0 S 0
miR-506-3p S, L S S, L S, L S, L 0 0 0 S 0 0
miR-663a S S S 0 0 S S S 0 0 S
miR-665 S, L S S, L S, L 0 0 S 0 S S S, L
miR-744-5p 0 0 0 0 S 0 S 0 0 0 0
miR-873-5p S, L 0 S, L 0 S S, L 0 0 0 0 0
[101]Open in a new tab
All miRNA-lncRNA interactions were validated with LncBase database. In
the table, S: Starbase database, L: LncBase database. “0” denotes there
is no interaction between the intended miRNA and lncRNA. Edges that
passed the validation process has been bolded.
Protein-miRNA-long non-coding RNA tripartite network
After the validation process, the final tripartite network was
constructed. This network consists of 7 proteins achieved from mapping
signatures to the reference genome, 15 high-degree miRNAs with 49
interactions retrieved from the protein-miRNA bipartite network and 11
high-degree lncRNAs with 45 interactions derived from the miRNA-lncRNA
bipartite network. [102]Figure 4 shows the final network.
FIGURE 4.
[103]FIGURE 4
[104]Open in a new tab
Final protein-miRNA-lncRNA tripartite network. The first layer (green
nodes) consists of 7 proteins. The second layer (blue nodes) consists
of 15 high degree miRNAs and the last layer (yellow nodes) consists of
11 high degree lncRNAs.
Scoring function results
We assigned a score to each ncRNA based on the scoring function and
arranged them in descending order. According to [105]Figure 5 and
[106]Table 6, the miRNAs with the highest scores are hsa-miR-199a-5p,
hsa-miR-199b-5p, and hsa-miR-3184. Moreover, KCNQ1OT1, NEAT1, and XIST
are the most significant lncRNAs. Each of the mentioned ncRNAs has a
score higher than the average. A literature review also confirms the
significance of the discussed ncRNAs.
FIGURE 5.
[107]FIGURE 5
[108]Open in a new tab
Scoring function results.
TABLE 6.
The score of miRNAs and lncRNA.
miRNA # of proteins lncRNA # of miRNAs
hsa-miR-199a-5p 4 KCNQ1OT1 10
hsa-miR-199b-5p 4 NEAT1 9
hsa-miR-423-5p 4 XIST 9
hsa-miR-3184 4 [109]AC010442.1 4
hsa-miR-124-3p 3 KRTAP5-AS1 3
hsa-miR-1908-5p 3 [110]AC092127.1 3
hsa-miR-506-3p 3 LINC00963 2
hsa-miR-4731-5p 3 [111]AC069281.2 2
hsa-miR-491-5p 3 [112]AC010327.5 1
hsa-miR-663a 3 STAG3LSP 1
hsa-miR-744-5p 3 SLC9A3-AS1 1
hsa-miR-665 3
hsa-miR-1286 3
hsa-miR-873-5p 3
hsa-miR-1321 3
[113]Open in a new tab
Boded miRNAs and lncRNAs are more important since their scores are
greater than the average score.
MicroRNA pathway enrichment analysis
The top 10 high-degree miRNAs from the protein-miRNA bipartite network
were applied to pathway enrichment analysis using the miRpathDB
database ([114]Vlachos et al., 2015). [115]Table 7 demonstrates that
the genes involved in Alzheimer’s disease pathway, Type II diabetes
pathway, Insulin signaling pathway, MAPK signaling pathway, PI3K-Akt
signaling pathway, mTOR signaling pathway, and Neurotrophin signaling
pathway are regulated by the significant miRNAs identified in this
study. The mentioned pathways have also been identified as shared
pathways between DM and AD in previous research.
TABLE 7.
MiRNA pathway enrichment analysis.
miRNA Pathway Database P-value Method
hsa-miR-199a-5p Alzheimer’s disease KEGG 0.04 Experimental
Type II diabetes mellitus KEGG 0.02 Experimental
Insulin signaling pathway KEGG 0.03 Experimental
mTOR signaling pathway KEGG 0.04 Experimental
MAPK signaling pathway KEGG 0.03 Experimental
Neurotrophin signaling pathway KEGG 0.01 Experimental
Neuronal System Reactome 0.02 Experimental
hsa-miR-199b-5p Diseases of signal transduction Reactome 0.03
Experimental
Regulation of insulin receptor GO-Biological Process 0.02 Experimental
Regulation of oxidative stress GO-Biological Process 0.03 Experimental
Cognition GO-Biological Process 0.03 Experimental
Neuron differentiation GO-Biological Process 0.04 Experimental
Insulin signaling pathway KEGG 0.02 Predicted
hsa-miR-423-5p mTOR signaling pathway KEGG 0.02 Predicted
MAPK signaling pathway KEGG 0.03 Predicted
Cell cycle KEGG 0.01 Experimental
Oxidative stress Reactome 0.04 Experimental
Synapse GO-Cellular Component 0.01 Experimental
Regulation of cell death GO-Biological Process 0.03 Experimental
mTOR signaling pathway KEGG 0.01 Predicted
Neurotrophin signaling pathway KEGG 0.01 Predicted
hsa-miR-3184 Alzheimer’s disease WikiPathways 0.04 Predicted
Insulin signaling pathway WikiPathways 0.02 Predicted
mTOR signaling pathway KEGG 0.01 Predicted
hsa-miR-124-3p Neurotrophin signaling pathway KEGG 0.04 Experimental
PI3K-Akt signaling pathway KEGG 0.02 Experimental
Lipid metabolism Reactome 0.03 Experimental
Insulin signaling pathway WikiPathways 0.02 Experimental
hsa-miR-1908-5p Aging GO-Biological Process 0.04 Experimental
Nervous system development GO-Biological Process 0.04 Experimental
Type II diabetes mellitus KEGG 0.009 Predicted
Insulin signaling pathway WikiPathways 0.04 Predicted
hsa-miR-506-3p Generation of neurons GO-Biological Process 0.02
Experimental
Insulin signaling pathway WikiPathways 0.01 Predicted
MAPK signaling pathway KEGG 0.01 Predicted
hsa-miR-4731-5p Neuronal System Reactome 0.00005 Predicted
Oxidative stress Reactome 0.002 Predicted
MAPK signaling pathway KEGG 0.02 Predicted
Insulin signaling pathway KEGG 0.007 Predicted
hsa-miR-491-5p PI3K-Akt signaling pathway KEGG 0.02 Experimental
Apoptosis KEGG 0.03 Experimental
AGE/RAGE pathway WikiPathways 0.03 Experimental
Oxidative stress Reactome 0.04 Experimental
hsa-miR-663a MAPK signaling pathway KEGG 0.01 Experimental
PI3K-Akt signaling pathway KEGG 0.02 Experimental
Apoptosis KEGG 0.04 Experimental
Aging GO-Biological Process 0.008 Experimental
mTOR signaling pathway KEGG 0.04 Predicted
Insulin signaling pathway KEGG 0.04 Predicted
hsa-miR-744-5p mTOR signaling pathway KEGG 0.005 Experimental
Insulin signaling pathway KEGG 0.0006 Experimental
Neurotrophin signaling pathway KEGG 0.02 Experimental
hsa-miR-665 Neuronal System Reactome 0.01 Predicted
hsa-miR-1286 mTOR signaling pathway KEGG 0.04 Predicted
Type II diabetes mellitus KEGG 0.03 Predicted
MAPK signaling pathway KEGG 0.006 Predicted
Neurotrophin signaling pathway KEGG 0.03 Predicted
hsa-miR-873-5p MAPK signaling pathway KEGG 0.02 Predicted
mTOR signaling pathway KEGG 0.02 Predicted
Neurotrophin signaling pathway KEGG 0.02 Predicted
hsa-miR-1321 Insulin signaling pathway KEGG 0.0008 Predicted
Neurotrophin signaling pathway KEGG 0.04 Predicted
mTOR signaling pathway KEGG 0.02 Predicted
[116]Open in a new tab
Discussion
Using a variety of data (SNPs, proteins, miRNAs, and lncRNAs), this
study investigated the relationship between DM and AD and introduced
candidate biomarkers that could be identified as potential drug targets
for the prevention and treatment of both diseases. In addition, the
current study demonstrated that the proposed candidate proteins could
involve known biomarkers in the cross-talk between DM and AD,
confirming the accuracy of the final tripartite network, which was
supported by the literature. Moreover, many of the proposed ncRNAs have
been identified in both diseases or in at least one of them.
Apolipoprotein E, the brain cholesterol transporter, appears to be
highly expressed in the brain, liver, kidney, and adipose tissue and
plays a crucial role in lipid metabolism. It is located on chromosome
19, and its allele number 4 (APOE4) is associated with AD. Previous
research has demonstrated that this protein plays an important role in
the progression of related diseases ([117]Peila et al., 2002; [118]Irie
et al., 2008; [119]Shinohara et al., 2020). Studies indicate that APOE
dysregulation can disrupt insulin signaling by preventing it from
interacting with its receptor, as well as affecting the clearance of
amyloid beta (Aβ) plaques in the brain. In addition, its negative
correlation with IDE brain expression may have an effect on Aβ plaques
([120]Du et al., 2009). Apolipoprotein C1 (APOC1), the downstream gene
of APOE, is involved in lipid metabolism and is predominantly expressed
in the brain and liver. Patients with T2DM have a high concentration of
this protein, which causes hyperlipidemia and, consequently, insulin
resistance ([121]Bouillet et al., 2016). APOC1 dysregulation has also
been detected in mice with memory loss ([122]Abildayeva et al., 2008).
In a previous GWAS study, specific alleles of “Translocase of Outer
Mitochondrial Membrane 40” (TOMM40) and “Nectin cell adhesion molecule
2” (NECTIN2; i.e., PVRL2) were found to be associated with an increased
risk of developing both DM and AD ([123]Hao et al., 2015). TOMM40, an
APOE-neighboring gene, is involved in age-related neurodegeneration
processes ([124]Gottschalk et al., 2014). Dysregulation of this protein
induces mitochondrial dysfunction, which interferes with insulin uptake
in the brain, resulting in insulin resistance ([125]Wennberg et al.,
2016). In addition, it has been reported that the expression of TOMM40
is reduced in the blood of Alzheimer’s patients ([126]Lee et al.,
2012). NECTIN2, a gene close to APOE, is a significant gene in AD
progression ([127]Hu et al., 2019). It is found in the brain and
neuronal cells and is necessary for proper cell junction formation. The
expression of this protein was found to be lower in T2DM patients
([128]Kleinstein et al., 2019).
In addition to the previously mentioned proteins, the major
histocompatibility complex, class I, C (HLA-C), the major
histocompatibility complex, class II, DQ alpha 1 (HLA-DQA1), and the
major histocompatibility complex, class II, DR beta 1 (HLA-DRB1) have
been linked to AD, Parkinson’s disease, and multiple sclerosis.
Additionally, their connection to DM has been documented. Studies
confirmed the role of HLA-DRB1 in the progression of AD ([129]Lu et
al., 2017) and T2DM ([130]Williams et al., 2011). Furthermore, the
dysregulation of HLA-DQA1 in AD ([131]Kwok et al., 2018) and T2DM has
been discussed ([132]Ma et al., 2013). [133]Russell et al. (2019) also
demonstrated the upregulation of HLA-C in Type 1 diabetes.
Hsa-miR-199a-5p, one of the miRNAs with the highest score as determined
by the scoring function (see [134]Table 6), has been cited in multiple
studies as the dysregulated miRNA in DM and AD. It appears to be
expressed in the brain and controls GLUT4, the brain glucose
transporter. GLUT4 is heavily expressed in the hippocampus and is
extremely important for hippocampal memory function ([135]McNay and
Pearson-Leary, 2020). It is upregulated in the prefrontal cortex of
Alzheimer’s patients ([136]Heidari et al., 2018) as well as diabetic
patients’ plasma ([137]Yan et al., 2014) which disrupts the regulation
of GLUT4, preventing neurons from absorbing insulin and causing insulin
resistance. An additional target of hsa-miR-199a-5p in the brain
peptidylprolyl cis/trans isomerase, NIMA-interacting 1 (PIN1), an
enzyme that regulates protein function in the post phosphorylation
process. It is believed that the lack of PIN1 (downregulation) causes
tau hyperphosphorylation in the brain ([138]Sultana et al., 2006).
Hsa-mir-199b-5p also interacts with PIN1. It is upregulated in the
brains of Alzheimer’s patients, and causes hyperphosphorylation of tau
protein ([139]Heidari et al., 2018).
Hsa-mir-124-3p has been downregulated in DM and AD. One of the most
important targets of this miRNA is Beta-secretase 1 (BACE1) which is a
significant known biomarker for AD. Decreasing the expression of
hsa-mir-124-3p causes increase in the levels of BACE1, leading to
hyperphosphorylation of the tau protein ([140]Fang et al., 2012;
[141]Lau et al., 2013).
Hsa-miR-1908-5p, which interacts with APOE in the tripartite network,
is involved in the regulation of human obesity and cholesterol. Wang et
al. demonstrated that upregulation of this miRNA in the peripheral
blood cells of Alzheimer’s patients could inhibit Aβ clearance
([142]Wang et al., 2019). In addition, the role of this microRNA in
cholesterol metabolism and neurodegeneration has been investigated
([143]Goedeke and Fernández-Hernando, 2014).
Hsa-miR-423-5p has been introduced as the novel differentially
expressed miRNA between non-AD individuals and Mild Cognitive
Impairment (MCI) patients ([144]Nagaraj et al., 2017). Alzheimer’s
patients experience an upregulation of hsa-mir-744-5p, which may
regulate PIN1 expression ([145]Lau et al., 2014). [146]Sidorkiewicz et
al. (2020), demonstrated the role of hsa-miR-491-5p in the diagnosis of
T2DM in prediabetic patients. They discussed the upregulation of this
miRNA as the diagnostic biomarker for T2DM ([147]Sidorkiewicz et al.,
2020). Downregulation of hsa-miR-665 has also been identified in T2DM
patients ([148]Yang et al., 2017). Additionally, [149]Sheinerman et al.
(2012) presented this miRNA as a novel plasma biomarker for the
diagnosis of MCI.
Alzheimer’s patients have been found to have altered expression of
hsa-miR-1321 ([150]Lau et al., 2014), hsa-mir-506-3p
([151]Nunez-Iglesias et al., 2010), hsa-miR-663a ([152]Grasso et al.,
2019), and hsa-miR-873-5p ([153]Shi et al., 2018). These miRNAs, along
with hsa-miR-4731-5p, hsa-miR-1286, and hsa-miR-3184-5p, have been
introduced in this study as novel candidate miRNAs in the cross-talk
between DM and AD for further analysis. The miRNAs identified in this
study were validated using the [154]Human microRNA Disease Database
(2007). Results show that hsa-miR-124-3p is a significant miRNA in T2DM
and AD. In addition, hsa-mir-665 and hsa-mir-199a-5p have been
identified as T2DM biomarkers, whereas hsa-miR-199b-5p and
hsa-miR-744-5p have been discovered in inflammatory and brain diseases,
respectively. As a plasma biomarker for MCI and glioblastoma, the
function of hsa-miR-491-5p has been discussed.
According to [155]Table 6, KCNQ1OT1 is the highest score lncRNA that is
highly expressed in the brain. In addition, the interaction between
KCNQ1OT1 and hsa-miR-506 has been identified in the plasma of patients
with elevated glucose levels ([156]Zhu et al., 2020).
Alzheimer’s patients have upregulated levels of NEAT1 and XIST. They
may result in the downregulation of hsa-miR-124-3p, which in turn
causes the upregulation of BACE1. As previously stated, BACE1
dysregulation results in tau hyperphosphorylation, which ultimately
leads to cell death. Therefore, silencing the aforementioned lncRNAs
may reduce AD-related complications ([157]Zhao et al., 2019; [158]Huang
et al., 2020; [159]Yue et al., 2020). In addition, NEAT1 was found to
be significantly upregulated in diabetic rats, leading to a highly
activated Akt/mTOR signaling pathway ([160]Huang et al., 2019). The
role of XIST in insulin resistance and T2DM has also been reported
([161]Sathishkumar et al., 2018).
Long intergenic non-coding RNA 963 (LINC00963) also modulates the Foxo
signaling pathway in rats and induces oxidative stress ([162]Chen et
al., 2018).
The role of other achieved lncRNAs has been less proven.
[163]AC011481.2, TSBP1, and BTNL2 Antisense RNA 1 (TSBP1-AS1) were
directly obtained by mapping signatures to the reference genome (see
[164]Table 2). [165]AC011481.2 is a novel antisense transcript for
NECTIN2. The GWAS catalog identifies mutations in specific loci
(rs6857) of this gene as a risk factor for developing T2DM and AD
simultaneously (2008). This mutation has been identified as one of 127
common SNPs in the cross-talk between DM and AD (see [166]Supplementary
Table 1). TSBP1-AS, KRTAP5-1/KRTAP5-2 antisense RNA 1 (KRTAP5-AS1),
STAG3LSP, SLC9A3 antisense RNA 1 (SLC9A3-AS1), [167]AC092127.1,
[168]AC010327.5, [169]AC069281.2, and [170]AC010442.1 have an unknown
function in DM and AD. Lack of information about lncRNAs may be the
reason. Due to the fact that they have been identified as significant
in our study, additional analysis may lead to noteworthy advancements.
[171]Table 8 provides a summary of the discussion.
TABLE 8.
Summary of discussion.
Candidate biomarker Significant target genes Description
hsa-miR-199a-5p GLUT4, PIN1 Upregulated in DM and AD, causes the
downregulation of target genes, resulted in hyperphosphorylation of Tau
and cell death.
hsa-miR-199b-5p PIN1 Upregulated in DM and AD, causes downregulation of
PIN1, resulted in hyperphosphorylation of Tau.
hsa-miR-124-3p BACE1, APOC1 Downregulated in DM and AD, causes
upregulation of target genes, resulted in hyperphosphorylation of Tau
and changing in lipid metabolism.
hsa-miR-1908-5p, hsa-miR-491-5p APOE Upregulated in DM and AD, causes
downregulation of APOE, resulted in failing the clearance of Aβ
plaques.
hsa-miR-423-5p NECTIN2, TOMM40, HLA-C, HLA-DQA1 Dysregulated in AD
causes changing in the regulation of target genes.
hsa-miR-744-5p APOE, PIN1 Upregulated in AD, causes downregulation of
target genes, resulted in failing the clearance of Aβ plaques and
hyperphosphorylation of Tau.
hsa-miR-665 APOE Downregulated in DM and AD, causes upregulation of
APOE, resulted in changing in lipid metabolism.
hsa-miR-506-3p, hsa-miR-663a, hsa-miR-873- 5p, hsa-miR-1321 APOE,
APOC1, TOMM40, NECTIN2, HLA-C, HLA-DQA1, HLA-DRB1 Dysregulated in AD.
Introduced in this study as novel miRNAs for further analysis.
hsa-miR-4731-5p, hsa-miR-1286, hsa-miR-3184-5p APOC1, TOMM40, NECTIN2,
HLA-C, HLA-DQA1, HLA-DRB1 Introduced in this study as novel miRNAs for
further analysis.
NEAT1, XIST has-miR-124-3p, BACE1 Upregulated in AD and DM, causes
downregulation of hsa-miR-124-3p, resulted in upregulation of BACE1 and
hyperphosphorylation of Tau.
KCNQ1OT1 hsa-miR-199a-5p, hsa-miR-199b-5p, hsa-miR-124-3p Dysregulated
of this lncRNAs could lead to oxidative stress.
LINC00963 has-miR-665 Causes oxidative stress by target Foxo signaling
pathway.
[172]AC011481.2 Increased risk of developing T2DM and AD simultaneously
by mutation in a specific loci (rs6857).
KRTAP5-AS1, STAG3LSP, SLC9A3-AS1, [173]AC092127.1, [174]AC010327.5,
[175]AC069281.2 hsa-miR-199a-5p, hsa-miR-199b-5p, hsa-423-5p Introduced
in this study as novel lncRNAs in the cross-talk between DM and AD.
[176]Open in a new tab
In future research, we will increase the reliability of our study by
evaluating the biomarkers we have introduced. In this regard, at the
Royan Institute for Stem Cells, we intend to conduct a mouse-based
laboratory experiment.
Conclusion
We identified 127 shared SNPs, 7 proteins, 15 miRNAs, and 11 lncRNAs in
the cross-talk between DM and AD. According to the literature review,
the proteins APOE, APOC1, TOMM40, NECTIN2, HLA-C, HLA-DRB1, and
HLA-DQA1, discovered in this study, play crucial roles in the
progression of both or one of the aforementioned diseases. Furthermore,
the scoring function revealed that hsa-miR-199a-5p, hsa-miR-199b-5p,
hsa-miR-423-5p, and hsa-miR-3184-5p (miRNAs with a score greater than
0.5) are the most significant miRNAs discovered in this study; the
first two are known miRNAs in the progression of corresponding
diseases, and the last two are proposed. This study also discovered
that NEAT1, XIST, and KCNQ1OT1 (lncRNAs with above-average scores) are
involved in the cross-talk between DM and AD. The first two lncRNAs are
known to play a role in the progression of disease (particularly AD),
and the third has been identified as a candidate for future research.
Finally, we proposed lncRNA/miRNA pairs; hsa-miR-124-3p/KCNQ1OT1,
hsa-miR-124-3p/NEAT1, and hsa-miR-124-3p/XIST, all of which are
expressed in the brain, as new candidate biomarkers for the occurrence
of DM and AD simultaneously. In addition, dysregulation in the
expression of hsa-miR-199a-5p in the blood of patients has been
suggested for early detection of both diseases.
Data availability statement
The original contributions presented in this study are included in the
article/[177]Supplementary material, further inquiries can be directed
to the corresponding authors.
Author contributions
ShG designed and performed experiments, analyzed data, and wrote the
manuscript. CE, SaG, KS, and MH-R supervised the research. All authors
read and approved the final manuscript.
Footnotes
^1
[178]http://www.nealelab.is/uk-biobank
^2
[179]https://ldlink.nci.nih.gov
^3
[180]https://genome.ucsc.edu/
^4
[181]https://www.snp-nexus.org/v4/
Funding
This work was financially supported the Research Council of the
University of Tehran is acknowledged.
Conflict of interest
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.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or claim
that may be made by its manufacturer, is not guaranteed or endorsed by
the publisher.
Supplementary material
The Supplementary Material for this article can be found online at:
[182]https://www.frontiersin.org/articles/10.3389/fnagi.2022.955461/ful
l#supplementary-material
[183]Click here for additional data file.^ (36.7KB, XLSX)
[184]Click here for additional data file.^ (17.9KB, XLSX)
[185]Click here for additional data file.^ (28.8KB, XLSX)
References