Abstract
Background
Public domain databases nowadays provide multiple layers of genome-wide
data e.g., promoter methylation, mRNA expression, and miRNA expression
and should enable integrative modeling of the mechanisms of regulation
of gene expression. However, researches along this line were not
frequently executed.
Results
Here, the public domain dataset of mRNA expression, microRNA (miRNA)
expression and promoter methylation patterns in four regions, the
frontal cortex, temporal cortex, pons and cerebellum, of human brain
were sourced from the National Center for Biotechnology Informations
gene expression omnibus, and reanalyzed computationally. A large number
of miRNA-mediated regulation of target genes and
miRNA-targeting-specific promoter methylation were identified in the
six pairwise comparisons among the four brain regions. The
miRNA-mediated regulation of target genes was found to be highly
correlated with one or both of miRNA-targeting-specific promoter
methylation and differential miRNA expression. Genes enriched for Kyoto
Encyclopedia of Genes and Genomes (KEGG) pathways that were related to
brain function and/or development were found among the target genes of
miRNAs whose differential expression patterns were highly correlated
with the miRNA-mediated regulation of their target genes.
Conclusions
The combinatorial analysis of miRNA-mediated regulation of target
genes, miRNA-targeting-specific promoter methylation and differential
miRNA expression can help reveal the brain region-specific
contributions of miRNAs to brain function and development.
Keywords: MicroRNA, Target gene regulation, Brain regions, Promoter
methylation, Pathway analysis
Background
miRNAs are short non-coding RNAs that are believed to suppress target
gene expression through the binding of miRNA “seed” regions to
complementary sequences of 3’ untranslated regions (UTR) of target
genes [[24]1]. miRNAs are generally assumed to regulate cellular
processes related to animal development [[25]2] and cellular
differentiation, and have been implicated in several diseases,
including cancer. Thus, miRNAs have been put forth as candidates for
tumor suppression [[26]3] and cancer biomarkers [[27]4]. miRNAs are
also known to be involved in reprogramming [[28]5]. As such, miRNAs are
considered to play critical roles in a wide range of biological
processes.
Recently, miRNA expression in the brain has attracted the interest of
many researchers [[29]6-[30]9]. Although there are extensive researches
about miRNA regulation of target genes [[31]6,[32]7], it is generally
believed that the expression of many genes is regulated by miRNAs
indirectly [[33]10]. In this sense, in order to understand miRNA
regulation of gene expression in brain regions, it is also important to
understand the mechanisms by which such regulation occurs.
Together with miRNAs, transcription factors (TFs) bind to promoter
regions and cooperatively regulate miRNA target genes [[34]11-[35]15].
TFs form a protein complex that binds to gene promoters during the
initiation of transcription. Since there are many TFs known to regulate
biological processes in regions of the brain [[36]16-[37]18], it is
natural to investigate the combinatorial effects of TFs and miRNA gene
regulation in the brain [[38]19,[39]20]. In contrast to what is known
about cooperative regulation by miRNA and TFs, investigations of gene
coregulation mediated by both miRNA and promoter methylation are
limited; however, siRNA-induced promoter methylation in CpG islands has
been reported [[40]21-[41]24]. Promoter methylation is generally
thought to suppress gene expression [[42]25]. Suppression of gene
expression by promoter methylation is often important. For example,
aberrant promoter methylation is often related to cancers
[[43]26,[44]27]. Promoter methylation also plays critical roles in
reprogramming [[45]28].
Despite the known importance of promoter methylation, the relationship
between promoter methylation and miRNA-mediated gene regulation has
received little attention. However, it was recently shown that
promoters of genes not targeted by miRNAs have higher levels of
methylation [[46]29]. We recently found that miRNA-targeting-specific
promoter methylation takes place in many cell lines [[47]30,[48]31].
miRNA-targeting-specific promoter methylation refers to the association
between 3 BUTR miRNA targetting and promoter methylation levels for a
given gene.
In this paper, we report that miRNA-targeting-specific promoter
methylaion also exists between distinct brain-regions in a brain-region
specific manner. Considered brain regions are frontal cortex, temporal
cortex, pons, and cerebellum [[49]32]. The frontal cortex is located at
the front of the head in human. It is considered to be the hub of most
higher functions and understanding, and is believed to govern most
behavioral traits, motor skills, and problem solving tactics [[50]33].
The temporal cortex is located in the lower right and left regions of
the brain, and is involved in hearing, understanding languages, face
recognition, and certain memory functions [[51]34]. The cerebellum is
located in the lower region at the back of the brain, and is central to
motion control [[52]35]. Finally, the pons is located in the center of
these three regions and mediates information transfer between several
other brain regions, including the cortex and cerebellum [[53]32].
Given the diverse functions of these brain regions, I hypothesized that
miRNA-targeting-specific promoter methylation would occur in a
region-specific manner. Not only did I determine that patterns of miRNA
regulation were indeed brain-region specific, I also revealed that some
miRNA regulation of target genes turned out to be controlled by not
only differential miRNA expression itself but also
miRNA-targeting-specific promoter methylaion. In addition, target genes
of miRNAs whose regulation was significantly correlated to differential
miRNA expression were also found to be enriched for
brain-region-specific functions and related KEGG pathways.
Methods
Patterns of miRNA and mRNA expression and promoter methylation
Datasets used in this study were downloaded from Gene Expression
Omnibus (GEO) under GEO ID [54]GSE15745. These included miRNA and mRNA
expression, and promoter methylation data from four distinct brain
regions (frontal cortex, temporal cortex, pons and cerebellum) in 150
human subjects [[55]36], which had been analyzed in detail in
connection with genomic variants, such as single nucleotide
polymorphisms and copy number variants; however, miRNA expression had
not been analyzed previously [[56]36]. Thus, in total, 600 tissue
samples were included. Processed signals were used without any further
normalization. For more details about data processing and analysis, see
the Supplementary Document (see Additional file [57]1).
Results and discussion
In this section, I will discuss the mutual relationships between
miRNA-related features and their biological meaning.
Mutual relationships between miRNA-mediated regulation of genes,
miRNA-targeting-specific promoter methylation, and differential miRNA
expression
I investigated miRNA-mediated gene regulation and
miRNA-targeting-specific promoter methylation in the frontal cortex,
temporal cortex, pons, and cerebellum of the human brain, based on the
P-values,
[MATH: Pmj,<ℓℓ<
/mi> :MATH]
s or
[MATH: Pmj,>ℓℓ′ :MATH]
s, which were used to estimate miRNA-mediated gene regulation and
miRNA-targeting-specific promoter methylation. Figure [58]1 illustrates
the results of this analysis. It is clear that target genes of a
substantial number of miRNAs are up/downregulated between these four
brain regions. It is also evident that some miRNA target genes are
differentially methylated between these four brain regions. This
strongly suggests that both miRNA-mediated gene regulation and
miRNA-targeting-specific promoter methylation play critical roles in
the development and function of these four brain regions. For example,
from the miRNA-centric point of view (Figure [59]1), compared to the
other three brain regions investigated, the pons has more genes with
hypermethylated promoters and lower expression levels, although these
characteristics are not always associated. This observation is
consistent with the general belief that the hypermethylation of
promoters is associated with reduced expression. This also signifies
that mRNA expression in the pons is distinct from the other three brain
regions.
Figure 1.
Figure 1
[60]Open in a new tab
Schematic illustration of the relationship between miRNA-mediated gene
regulation and miRNA-targeted-specific promoter methylation.
Arrows/segments indicate up/downregulation of miRNA target genes (a)
and miRNA-targeted-specific promoter methylation (b). Black (red)
numbers next to inequality signs are the averaged number of miRNAs
whose target genes are significantly up/downregulated (whose target
genes promoters are hyper/hypomethylated). TCTX refers to the temporal
cortex, FCTX to the frontal cortes, CRBLM to the cerebellum, and PONS
to the pons. For example, there are 280 (183) miRNAs whose target gene
promoters are significantly hyper(hypo)methylated in FCFX compared to
TCTX. Similarly, there are 889 (173) miRNAs whose target genes are
significantly up(down)regulated in FCFX compared to TCTX. Since 280 is
larger than 183, gene promoters in FCFX are considered to be more
hypermethylated than those in TCTX based on the miRNA-centric-view,
thus the red arrow directs the reader from TCTX to FCTX. Likewise,
because 889 is larger than 173, genes in FCFX are considered to be
upregulated when compared to TCTX, thus the black arrow directs the
reader from TCTX to FCTX. The numbers in the rectangle indicate
Spearman correlation coefficients between miRNA-mediated gene
regulation and miRNA-targeted-specific promoter methylation,
[MATH:
ρℓℓ<
/mrow>′mRNA,Methyl. :MATH]
. Standard deviations of Spearman correlation coefficients,
[MATH:
Δρℓ<
mi>ℓ′mRNA,Methyl. :MATH]
are shown in parentheses.
Mutual relationships between miRNA-mediated regulation of target genes and
miRNA-targeting-specific promoter methylation
In order to understand the mutual relationship between miRNA-mediated
gene regulation and miRNA-targeting-specific promoter methylation, I
computed the correlation coefficient of the mean rank of P-values,
[MATH:
ρℓℓ<
/mrow>′mRNA,Methyl. :MATH]
, for six pairwise comparisons between the frontal cortex, temporal
cortex, pons, and cerebellum (see Figure [61]1). Here, the means were
taken over all samples in each brain region. Excluding a single
pairwise comparison between the cerebellum and pons, correlation
coefficients for the remaining five comparisons varied between 0.25 and
0.51. These values were considered to be sufficiently large taking into
account the fact that the number of P-values in a given brain region is
as large as M, the number of miRNAs comsidered. The P-values of each
correlation coefficient are less than 2.2×10^−16. This means, the
correlation between miRNA-mediated gene regulation and
miRNA-targeting-specific promoter methylation is highly significant
independent of pairs of brain regions. The smallest correlation
coefficients were observed in the cerebellum and pons. Although the
correlation coefficient was large in aggregate (0.09), individual
P-value was as small as 4×10^−5, which is highly significant.
In order to confirm the correlation between miRNA-mediated gene
regulation and miRNA-targeting-specific promoter methylation, the root
mean squared averages of the correlation coefficients in each sample,
[MATH:
Δρℓ<
mi>ℓ′mRNA,Methyl. :MATH]
, were also computed. Excluding pairwise comparisons for the frontal
cortex and pons for which the absolute value of
[MATH:
ρℓℓ<
/mrow>′mRNA,Methyl. :MATH]
was the maximum,
[MATH:
Δρℓ<
mi>ℓ′mRNA,Methyl. :MATH]
was larger than the absolute value of
[MATH:
ρℓℓ<
/mrow>′mRNA,Methyl. :MATH]
. This signifies that the correlation coefficients within each sample
were not small, but that when averaged over all samples, the value was
seemingly small because of the occurrence of both positive and negative
correlations with equal probabilities. Thus, I conclude that
miRNA-mediated regulation and miRNA-targeting-specific promoter
methylation are significantly correlated. Worth noting is that the
signs of correlation coefficients,
[MATH:
ρℓℓ<
/mrow>′mRNA,Methyl. :MATH]
, are neither definitively positive nor negative. One may think that
they should be positive, as both promoter methylation and miRNA
targeting should suppress gene expression. However, because genes
targeted by miRNAs are expected to be downregulated (upregulated) only
when miRNA itself is upregulated (downregulated), there is no reason to
expect that the correlation coefficients between miRNA-mediated gene
regulation and miRNA-targeting-specific promoter methylation should
always take positive or negative values.
Relationships between miRNA-mediated regulation of target genes,
miRNA-targeting-specific promoter methylation and differential miRNA
expression
In order to determine the relationship between miRNA-mediated gene
regulation,
[MATH: Pmj,<ℓℓ′ :MATH]
or
[MATH: Pmj,>ℓℓ′ :MATH]
, and differential expression of miRNA,
[MATH: logxmjℓ
xmjℓ′
:MATH]
, the correlation coefficients were computed. However, these
correlation coefficients were too small to be significant (not shown
here). This seemingly contradicts the observed correlation between
miRNA-mediated gene regulation and miRNA-targeted-specific promoter
methylation.
Thus, in order to resolve this apparent discrepancy, I employed
multivariate regression models between miRNA-mediated gene regulation,
miRNA-targeting-specific promoter methylation, and differential miRNA
expression, also considering both sample gender and age (see Methods).
In contrast to the above discrepancy, depending upon the miRNA
considered, I identified significant correlations between only selected
variables that were included in the regression model. In other words, I
found that all of the variables were not always correlated, but were
instead selectively correlated. In order to quantize these
correlations, for each miRNA, I picked out the combinations of
variables that were significantly correlated (see Methods). Table [62]1
lists the miRNAs selected for each pair of brain regions based on the
criterion described in the subsection, “The selection of miRNAs that
significantly regulate target genes based on multiple regression” in
Supplementary Document (see Additional file [63]1), i.e., miRNAs whose
differential expression is significantly correlated to miRNA-mediated
gene regulation. To our knowledge, this is the first time that miRNA
gene regulation has been shown to be mediated by both differential
miRNA expression and miRNA-targeting-specific promoter methylation.
Table 1.
miRNAs that significantly regulate target genes
CRBLM vs FCTX
__________________________________________________________________
CRBLM vs PONS
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CRBLM vs TCTX
__________________________________________________________________
Reciprocal Nonreciprocal Reciprocal Nonreciprocal Reciprocal
Nonreciprocal
hsa-miR-181c-5p
__________________________________________________________________
hsa-miR-200a-5p
__________________________________________________________________
hsa-miR-20a-5p
__________________________________________________________________
hsa-let-7b-5p
__________________________________________________________________
hsa-miR-210
__________________________________________________________________
hsa-miR-99a-5p
__________________________________________________________________
hsa-miR-135a-5p
__________________________________________________________________
hsa-miR-381
__________________________________________________________________
hsa-miR-23a-3p
__________________________________________________________________
hsa-let-7e-5p
__________________________________________________________________
__________________________________________________________________
hsa-miR-191-5p
__________________________________________________________________
hsa-miR-137 *
__________________________________________________________________
hsa-miR-202-3p *
__________________________________________________________________
hsa-miR-148a-3p
__________________________________________________________________
hsa-miR-197-3p
__________________________________________________________________
__________________________________________________________________
hsa-miR-99b-5p *
__________________________________________________________________
hsa-miR-363-3p
__________________________________________________________________
hsa-miR-561-3p
__________________________________________________________________
hsa-miR-10a-5p
__________________________________________________________________
hsa-miR-181b-5p
__________________________________________________________________
__________________________________________________________________
hsa-miR-617
__________________________________________________________________
hsa-miR-369-3p
__________________________________________________________________
hsa-miR-568
__________________________________________________________________
hsa-miR-221–3p
__________________________________________________________________
hsa-let-7i-5p
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
hsa-miR-487a *
__________________________________________________________________
hsa-miR-618
__________________________________________________________________
hsa-miR-223–3p
__________________________________________________________________
hsa-miR-9-5p
__________________________________________________________________
FCFX vs PONS
__________________________________________________________________
hsa-miR-514a-3p
__________________________________________________________________
hsa-miR-630 *
__________________________________________________________________
hsa-miR-1
__________________________________________________________________
hsa-miR-126-3p
__________________________________________________________________
hsa-miR-365a-3p
__________________________________________________________________
hsa-miR-302d-3p
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hsa-miR-553
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hsa-miR-133a
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hsa-miR-134
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hsa-miR-378a-5p
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hsa-miR-432-5p
__________________________________________________________________
hsa-miR-554
__________________________________________________________________
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hsa-miR-137 *
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hsa-miR-154-3p
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hsa-miR-595
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hsa-miR-655
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hsa-miR-146a-5p
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hsa-miR-299-5p
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
hsa-miR-421
__________________________________________________________________
__________________________________________________________________
hsa-miR-452-5p
__________________________________________________________________
hsa-miR-99b-5p *
__________________________________________________________________
FCTX vs TCTX
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
hsa-miR-484
__________________________________________________________________
hsa-miR-377-3p
__________________________________________________________________
has-miR-373-3p
__________________________________________________________________
hsa-miR-24-3p
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
hsa-miR-511
__________________________________________________________________
hsa-miR-383
__________________________________________________________________
__________________________________________________________________
hsa-miR-485-5p
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
hsa-miR-515-5p
__________________________________________________________________
hsa-miR-431-5p
__________________________________________________________________
__________________________________________________________________
hsa-miR-766-3p
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
hsa-miR-571
__________________________________________________________________
hsa-miR-329
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
hsa-miR-549
__________________________________________________________________
hsa-miR-485-3p
__________________________________________________________________
PONS VS TCTX
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
hsa-miR-487a *
__________________________________________________________________
hsa-miR-9-3p
__________________________________________________________________
hsa-miR-222-3p
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
hsa-miR-202-3p *
__________________________________________________________________
hsa-miR-302a-3p
__________________________________________________________________
hsa-miR-125b-5p
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
hsa-miR-432-3p
__________________________________________________________________
hsa-miR-410
__________________________________________________________________
hsa-miR-328
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
hsa-miR-495
__________________________________________________________________
hsa-miR-487b
__________________________________________________________________
hsa-miR-581
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
hsa-miR-504
__________________________________________________________________
hsa-miR-630 *
__________________________________________________________________
hsa-miR-661
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
hsa-miR-505-3p
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
hsa-miR-563
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
hsa-miR-578
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
hsa-miR-630 *
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
hsa-miR-668
[64]Open in a new tab
miRNAs predicted to regulate target genes based on six pairwise
comparisons among four brain regions: the frontal cortex (FCTX),
temporal cortex (TCTX), pons (PONS), and cerebellum (CRBLM). The labels
“Reciprocal” and “nonreciprocal” indicate whether the observed
relationship between miRNA expression and target gene mRNA was either
reciprocal or nonreciprocal. Asterisked miRNAs appear more than once.
Bold faced miRNAs were previously reported to be related to brain
development/diseases [[65]37-[66]39]. See subsection “The selection of
miRNAs that significantly regulate target genes based on multiple
regression model” in Supplementary Document (see Additional file [67]1)
for the detailed criterion of miRNAs selection.
Biological meanings of findings
As can be seen in Table [68]1, miRNAs selected for each pair of brain
regions are not unique, but rather divergent. Some of the listed miRNAs
were previously reported to be important in specific brain regions. For
example, Yao et al recently investigated miRNA expression in the rat
cerebral cortex during brain development [[69]37]. Many of the top 20
most highly expressed miRNAs identified by Yao et al at each of eight
different developmental stages, ranging from early developmental stages
to late post natal stages, were also significant in our dataset
(rno-let-7b, 7e, 7i, rno-miR-181b, 99a/b, 9, 125b-5p, and 191). Yao et
al also emphasized the importance of miR-137, the ortholog of the human
miRNA, hsa-miR-137; this miRNA was found to be significant twice in our
analysis, compared to the most of other miRNAs which were only
identified as significant once. In addition, many of the miRNAs listed
in Table [70]1 have also been previously implicated in brain diseases,
including Alzheimers disease (AD), Parkinsons disease (PD), Huntingtons
disease (HD), and various other neurodegenerative disorders
[[71]38,[72]39]. This overlap lends support to the utility of our
method for identifying miRNAs with potential functional relevance in
the brain. Although there have been other investigations of brain miRNA
expression, to our knowledge, I am the first to interrogate miRNA
expression data across multiple brain regions.
In order to better understand the biological functions of the miRNA
targets identified in our analysis, I employed pathway analysis (Table
[73]2), which has been shown previously to be effective for miRNA
target genes (e.g., [[74]40,[75]41]). For this purpose, I used
DIANA-mirPath [[76]42], which is a web tool developed for KEGG pathway
enrichment analysis of miRNA target genes.
Table 2.
miRNA target genes KEGG pathway enrichment
__________________________________________________________________
__________________________________________________________________
CRBLM
__________________________________________________________________
CRBLM
__________________________________________________________________
CRBLM
__________________________________________________________________
FCTX
__________________________________________________________________
FCTX
__________________________________________________________________
PONS
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
vs
__________________________________________________________________
vs
__________________________________________________________________
vs
__________________________________________________________________
vs
__________________________________________________________________
vs
__________________________________________________________________
vs
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
FCTX
__________________________________________________________________
PONS
__________________________________________________________________
TCTX
__________________________________________________________________
PONS
__________________________________________________________________
TCTX
__________________________________________________________________
TCTX
__________________________________________________________________
KEGG pathways R N R N R N R N R N R N
1
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TGF- β signaling pathway
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○
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2
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Glioma ∗
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3
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MAPK signaling pathway
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4
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Axon guidance ∗
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5
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Phosphatidylinositol signaling system
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○
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6
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mTOR signaling pathway
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7
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Adipocytokine signaling pathway
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8
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Pancreatic cancer
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9
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Endocytosis
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10
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Focal adhesion
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11
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Insulin signaling pathway
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12
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Neurotrophin signaling pathway ∗
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13
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Colorectal cancer
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14
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Arrhythmogenic right ventricular cardiomyopathy (ARVC)
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15
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Wnt signaling pathway
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16
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Non-small cell lung cancer
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17
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Adherens junction
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18
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ErbB signaling pathway
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19
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Pathways in cancer
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20
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Glycosaminoglycan biosynthesis - heparan sulfate
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21
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Type II diabetes mellitus
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22
__________________________________________________________________
Melanoma
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23
__________________________________________________________________
Renal cell carcinoma
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24
__________________________________________________________________
Inositol phosphate metabolism
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25
__________________________________________________________________
Chronic myeloid leukemia
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○
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○
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26
__________________________________________________________________
T cell receptor signaling pathway
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27
__________________________________________________________________
Small cell lung cancer
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28
__________________________________________________________________
Fc gamma R-mediated phagocytosis
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29
__________________________________________________________________
Prostate cancer
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30
__________________________________________________________________
Salivary secretion
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31
__________________________________________________________________
Osteoclast differentiation
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32
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Regulation of actin cytoskeleton
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33
__________________________________________________________________
Endocrine and other factor-regulated calcium reabsorption
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○
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34
__________________________________________________________________
Lysine degradation
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35
__________________________________________________________________
Circadian rhythm - mammal
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36
__________________________________________________________________
Glycosaminoglycan biosynthesis - chondroitin sulfate
__________________________________________________________________
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37
__________________________________________________________________
N-Glycan biosynthesis
__________________________________________________________________
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38
__________________________________________________________________
Long-term depression
__________________________________________________________________
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○
__________________________________________________________________
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○
__________________________________________________________________
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39
__________________________________________________________________
Prion diseases
__________________________________________________________________
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○
__________________________________________________________________
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40
__________________________________________________________________
ECM-receptor interaction
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41
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Tight junction
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42
__________________________________________________________________
Hypertrophic cardiomyopathy (HCM)
__________________________________________________________________
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○
__________________________________________________________________
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__________________________________________________________________
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43
__________________________________________________________________
Cell adhesion molecules (CAMs)
__________________________________________________________________
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○
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__________________________________________________________________
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44
__________________________________________________________________
Dilated cardiomyopathy
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__________________________________________________________________
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45
__________________________________________________________________
Fatty acid biosynthesis
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46
__________________________________________________________________
Long-term potentation
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47
__________________________________________________________________
Ubiquitin mediated proteolysis
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48
__________________________________________________________________
Thyroid cancer
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__________________________________________________________________
__________________________________________________________________
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49
__________________________________________________________________
Notch signaling pathway
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__________________________________________________________________
__________________________________________________________________
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50
__________________________________________________________________
Mismatch repair
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51
__________________________________________________________________
Acute myeloid leukemia
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__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
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52
__________________________________________________________________
Glycosphingolipid biosynthesis - lacto and neolacto series
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○
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53
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Glycosaminoglycan biosynthesis - keratan sulfate
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54
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Biotin metabolism
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55
__________________________________________________________________
Gap junction
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56
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Gastric acid secretion
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57
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Taurine and hypotaurine metabolism
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58
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Aldosterone-regulated sodium reabsorption
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59
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GnRH signaling pathway
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__________________________________________________________________
__________________________________________________________________
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60
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Metabolism of xenobiotics by cytochrome P450
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61
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Mucin type O-Glycan biosynthesis
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62
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Biosynthesis of unsaturated fatty acids
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63
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Viral myocarditis
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64
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Cytokine-cytokine receptor interaction
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○
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65
__________________________________________________________________
Hematopoietic cell lineage
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__________________________________________________________________
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66 Valine, leucine and isoleucine biosynthesis ○
[77]Open in a new tab
KEGG pathways marked with ○ are enriched by target genes of miRNAs
selected in Table [78]1. “R” and “N” indicate whether the relationship
between miRNA expression and target gene mRNA is reciprocal
(nonreciprocal). Pathways asterisked and bold faced are directly
related to brain and neurons, respectively, and discussed in detail in
the Supplementary Document (see Additional file [79]1).
Compared to the variation observed in the miRNAs listed in Table [80]1,
KEGG pathways for miRNA targets (Table [81]2) are highly universal and
biologically meaningful as shiwn in the followings. For example, Paul
et al[[82]43] measured and analyzed transcritpomes in the mouse
cerebellum. Cells were classified into Purkinje cells (PCs) at
postnatal days 3, 7, 14, 21, 28, 35, and 56 (P3, P7, P14, P21, P28,
P35, and P56), and the mixture of Stellate/Basket cells (StCs/BKCs) at
P14, P21, P28, P35, and P56. They conducted pathway enrichment analysis
using KEGG pathways based on developmental gene expression of PCs and
S/BCs. From this, they found that many pathways were enriched at
several different time points. In their data, upregulated genes
identified between P3-PCs and P7-PCs were enriched for pathways such as
“axon guidance”, “regulation of actin cytoskeleton”, “gap junction”,
and “tight junctions”, implicating roles for these genes in the early
stages of circuit integration by PCs. These changes are accompanied by
an upregulation of other pathways such as insulin, TGF- β, Hedgehog,
and Wnt signaling, which are important for axon guidance. The
upregulation of GnRH signaling, which is known to have a modulatory
effect on cerebellar neurons and P53 signaling, and is important for PC
survival was also observed during this time.
In P14-PCs, Paul et al also reported that pathways related to
“long-term potentiation”, “long-term depression”, “JAK/STAT”, “VEGF”,
and “mTOR signaling” were elevated, which correlate to the development
of parallel fiber synapses. Between P28 and P56, the upregulation of
pathways related to “CAMs”, “chondroitin sulfate biosynthesis”, “focal
adhesion”, “cytokine receptor interaction”, and “extracellular matrix
receptor interaction” (ECM-interaction) correlate with the maturation
and stabilization of PC connectivity. In S/BCs a number of similar
pathways are also activated. “axon guidance”, “tight junction”,
“adherens junction”, “insulin signaling”, “ErbB”, and “spliceosome”
pathways were upregulated in P14S/BCs, reflecting delayed axogenesis of
BskC and StC after they enter the ML during the second postnatal week.
However, between P28 and P35, similar to PC cells, pathways of
“ECM-receptor interaction”, “CAMs”, “cytokine receptor interaction”,
“neuroactive ligand receptor interactions” and “regulation of
cytoskeleton” were activated. These listed pathways largely overlap
those listed in Table [83]2. Although Paul et al mainly investigated
the cerebellum, I studied the cerebellum, as well as the pons, and
frontal and temporal cortex; thus, I investigated previous studies
related to individual pathways listed in Table [84]2 one by one.
Pathways directly related to brain/nervous system
Some pathways listed in Table [85]2 are obviously related to the brain
and/or nervous system. For example, “axon guidance” is definitely
included in brain development. “Glioma” is a brain tumor and the
“neurotrophin signaling pathway” is related to neural systems.
Enrichments in these three pathways further supports the notion that
the genes I have identified are indeed relevant to brain function and
development. For additional discussion of other selected pathways, see
the Supplementary Document (see Additional file [86]1).
Other notable observations
For further discussion of the divergence of miRNAs selected vs. the
uniformity of pathways selected, positive vs. negative correlations
between miRNA expression and target gene expression, and possible
biological explanations underlying coregulation by both miRNA and
promoter methylation, see the Supplementary Document (see Additional
file [87]1).
Conclusion
In this paper, I demonstrated possible miRNA coreguation of target
genes in brain regions by analyzing both differential miRNA expression
and miRNA-targeting-specific promoter methylation. Selected miRNAs were
enriched in brain-related KEGG pathways. Because this was simply
descriptive and no mechanisms responsible for the cooperative
regulation described above were presented, experiment-based follow up
studies will be necessary to validate our findings.
Competing interests
The author declares that they have no competing interests.
Supplementary Material
Additional file 1
Supplementary Document: Supplementary information not included in the
paper.
[88]Click here for file^ (128.8KB, pdf)
Acknowledgements