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BMC Genomics

BioMed Central

Open Access

Research article

Meta-coexpression conservation analysis of microarray data: a
"subset" approach provides insight into brain-derived neurotrophic
factor regulation
Tamara Aid-Pavlidis*†1, Pavlos Pavlidis†2 and Tõnis Timmusk1
Address: 1Department of Gene Technology, Tallinn University of Technology, Akadeemia tee 15, 19086 Tallinn, Estonia and 2Department of
Biology, Section of Evolutionary Biology, University of Munich, Grosshaderner Strasse 2, 82152 Planegg-Martinsried, Germany
Email: Tamara Aid-Pavlidis* - tamara.aid@gmail.com; Pavlos Pavlidis - pavlidis@zi.biologie.uni-muenchen.de;
Tõnis Timmusk - tonis.timmusk@ttu.ee
* Corresponding author †Equal contributors

Published: 8 September 2009
BMC Genomics 2009, 10:420

doi:10.1186/1471-2164-10-420

Received: 29 December 2008
Accepted: 8 September 2009

This article is available from: http://www.biomedcentral.com/1471-2164/10/420
© 2009 Aid-Pavlidis et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract
Background: Alterations in brain-derived neurotrophic factor (BDNF) gene expression contribute
to serious pathologies such as depression, epilepsy, cancer, Alzheimer's, Huntington and
Parkinson's disease. Therefore, exploring the mechanisms of BDNF regulation represents a great
clinical importance. Studying BDNF expression remains difficult due to its multiple neural activitydependent and tissue-specific promoters. Thus, microarray data could provide insight into the
regulation of this complex gene. Conventional microarray co-expression analysis is usually carried
out by merging the datasets or by confirming the re-occurrence of significant correlations across
datasets. However, co-expression patterns can be different under various conditions that are
represented by subsets in a dataset. Therefore, assessing co-expression by measuring correlation
coefficient across merged samples of a dataset or by merging datasets might not capture all
correlation patterns.
Results: In our study, we performed meta-coexpression analysis of publicly available microarray
data using BDNF as a "guide-gene" introducing a "subset" approach. The key steps of the analysis
included: dividing datasets into subsets with biologically meaningful sample content (e.g. tissue,
gender or disease state subsets); analyzing co-expression with the BDNF gene in each subset
separately; and confirming co- expression links across subsets. Finally, we analyzed conservation in
co-expression with BDNF between human, mouse and rat, and sought for conserved overrepresented TFBSs in BDNF and BDNF-correlated genes. Correlated genes discovered in this study
regulate nervous system development, and are associated with various types of cancer and
neurological disorders. Also, several transcription factor identified here have been reported to
regulate BDNF expression in vitro and in vivo.
Conclusion: The study demonstrates the potential of the "subset" approach in co-expression
conservation analysis for studying the regulation of single genes and proposes novel regulators of
BDNF gene expression.

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Background
The accumulation of genome-wide gene expression data
has enabled biologists to investigate gene regulatory
mechanisms using system biology approaches. Recent
developments in microarray technologies and bioinformatics have driven the progress of this field [1]. Moreover,
publicly available microarray data provide information
on human genome-wide gene expression under various
experimental conditions, which for most researchers
would be difficult to access otherwise.
BDNF (brain-derived neurotrophic factor) plays an
important role in the development of the vertebrates'
nervous system [2]. BDNF supports survival and differentiation of embryonic neurons and controls various neural
processes in adulthood, including memory and learning
[3], depression [4], and drug addiction [5]. Alterations in
BDNF expression can contribute to serious pathologies
such as epilepsy, Huntington, Alzheimer's, and Parkinson's disease [6]. Alteration in BDNF expression is associated with unfavorable prognosis in neuroblastoma [7],
myeloma [8], hepatocellular carcinoma [9] and other
tumors [10]. Apart from brain, expression of alternative
BDNF transcripts has been detected in a variety of tissues
(such as heart, muscle, testis, thymus, lung, etc.) [11,12].
Numerous studies have been conducted to unravel the
regulation of BDNF expression in rodents and human.
Data on the structure of human [11] and rodent
[12]BDNF gene have been recently updated. Nevertheless,
little is known about the regulation of human BDNF gene
expression in vivo. Unraveling the regulation of BDNF
expression remains difficult due to its multiple activitydependent and tissue-specific promoters. Thus, analysis of
the gene expression under various experimental conditions using microarray data could provide insight into the
regulation of this complex gene.
Meta-coexpression analysis uses multiple experiments to
identify more reliable sets of genes than would be found
using a single data set. The rationale behind meta-coexpression analysis is that co-regulated genes should display
similar expression patterns across various conditions.
Moreover, such analysis may benefit from a vast representation of tissues and conditions [13]. A yeast study
showed that the ability to correctly identify co-regulated
genes in co-expression analysis is strongly dependent on
the number of microarray experiments used [14]. Another
study that examined 60 human microarray datasets for coexpressed gene pairs reports that gene ontology (GO)
score for gene pairs increases steadily with the number of
confirmed links compared to the pairs confirmed by only
a single dataset [15]. Several studies have successfully
applied meta-analysis approach to get important insights
into various biological processes. For instance, microarray
meta-analysis of aging and cellular senescence led to the

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observation that the expression pattern of cellular senescence was similar to that of aging in mice, but not in
humans [16]. Data from a variety of laboratories was integrated to identify a common host transcriptional response
to pathogens [17]. Also, meta-coexpression studies have
displayed their efficiency to predict functional relationships between genes [18]. However, co-expression alone
does not necessarily imply that genes are co-regulated.
Thus, analysis of evolutionary conservation of co-expression coupled with the search for over-represented motifs
in the promoters of co-expressed genes is a powerful criterion to identify genes that are co-regulated from a set of
co-expressed genes [19,20].
In co-expression analysis, similarity of gene expression
profiles is measured using correlation coefficients (CC) or
other distance measures. If the correlation between two
genes is above a given threshold, then the genes can be
considered as «co-expressed» [1]. Co-expression analysis
using a «guide-gene» approach involves measuring CC
between pre-selected gene(s) and the rest of the genes in a
dataset.
It is a common practice in meta-coexpression studies to
assess co-expression by calculating the gene pair correlations after merging the datasets [20] or by confirming the
re-occurrence of significant correlations across datasets
[15]. However, it has been shown recently that genes can
reveal differential co-expression patterns across subsets in
the same dataset (e.g. gene pairs that are correlated in normal tissue might not be correlated in cancerous tissue or
might be even anti-correlated) [21]. Therefore, assessing
co-expression by measuring CC across merged samples of
a dataset or by merging datasets may create correlation
patterns that could not be captured using the CC measurement.
In this study, we performed co-expression analysis of publicly available microarray data using BDNF as a "guidegene". We inferred BDNF gene co-expression links that
were conserved between human and rodents using a
novel "subset" approach. Then, we discovered new putative regulatory elements in human BDNF and in BDNFcorrelated genes, and proposed potential regulators of
BDNF gene expression.

Results
We analyzed 299 subsets derived from the total of 80
human, mouse and rat microarray datasets. In order to
avoid spurious results that could arise from high-throughput microarray analysis methods, we applied successive
filtering of genes. Then, we divided datasets into subsets
with biologically meaningful sample content (e.g. tissue,
gender or disease state subsets), analyzed co-expression
with BDNF across samples separately in each subset and

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confirmed the links across subsets. Finally, we analyzed
conservation in co-expression between human, mouse
and rat, and sought for conserved TFBSs in BDNF and
BDNF-correlated genes (Figure 1).
Data filtering
Gene Expression Omnibus (GEO) from NCBI and
ArrayExpress from EBI are the largest public peer reviewed
microarray repositories, each containing about 8000
experiments. In order to avoid inaccuracies arising from
measuring expression correlation across different microarray platforms [13] we used only Affymetrix GeneChips
platforms for the analysis. Since ArrayExpress imports
Affymetrix experiments from GEO http://www.ebi.ac.uk/
microarray/doc/help/GEO_data.html, we used only GEO
database to retrieve datasets.

A study examining the relationship between the number
of analyzed microarray experiments and the reliability of
the results reported that the accuracy of the analysis plateaus at between 50 and 100 experiments [14]. Another
study demonstrated how the large amount of microarray
data can be exploited to increase the reliability of inferences about gene functions. Links that were confirmed
three or more times between different experiments had
significantly higher GO term overlaps than those seen
only once or twice (p < 10-15) [15]. Therefore, we performed meta-coexpression analysis using multiple experiments to increases the accuracy of the prediction of the coexpression links.
Since BDNF served as a guide-gene for our microarray
study, qualitative and quantitative criteria were applied
for selection of the experiments with respect to BDNF
probe set presence on the platform [see Additional file 1:
BDNF probe sets], BDNF signal quality and expression
levels. In addition, non-specific filtering [19] was performed to eliminate the noise (see Methods/Microarray
datasets). Consequently, 80 human, mouse and rat microarray experiments (datasets) from Gene Expression Omnibus (GEO) database met the selection criteria. Each
dataset was split into subsets according to the annotation
file included in the experiment [see Additional file 2:
Microarray datasets and Additional file 3: Subsets]. In
summary, 299 subsets were obtained from 38 human, 24
mouse and 18 rat datasets. From 38 human datasets, 8
were related to neurological diseases (epilepsy, Huntington's, Alzheimer's, aging, encephalitis, glioma and schizophrenia) and contained samples from human brain;
another 9 datasets contained samples from human "normal" (non-diseased) tissues (non-neural, such as blood,
skin, lung, and human brain tissues); 12 datasets had
samples from cancerous tissues of various origins (lung,
prostate, kidney, breast and ovarian cancer). The rest 9
datasets contained samples from diseased non-neural tissues (HIV infection, smoking, stress, UV radiation etc.).

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Out of 24 mouse datasets, 5 datasets were related to neurological diseases (brain trauma, spinal cord injury,
amyotrophic lateral sclerosis, and aging); 15 datasets contained normal tissue samples (neural and peripheral tissues); 1 dataset contained lung cancer samples; 3 datasets
were related to non-neural tissues' diseases (muscle dystrophy, cardiac hypertrophy and asthma). Among 18 rat
datasets, 11 datasets were related to neurological diseases
(spinal cord injury, addiction, epilepsy, aging, ischemia
etc), 5 datasets were with "normal tissue samples" composition and 2 datasets examined heart diseases [see Additional file 2: Microarray datasets].
According to Elo and colleagues [22] the reproducibility
of the analysis of eight samples approaches 55%. Selecting
subsets with more than eight samples for the analysis
could increase the reproducibility of the experiment however reducing the coverage, since subsets with lower
number of samples would be excluded. Thus, we selected
subsets with a minimum of eight samples for the analysis,
in order to achieve satisfactory reproducibility and coverage. The expression information for human, mouse and
rat genes obtained from GEO database, information
about BDNF probe names used for each dataset, information about subsets derived from each experiment, and
data on correlation of expression between BDNF and
other genes for each microarray subset has been made
available online and can be accessed using the following
link: http://www.bio.lmu.de/~pavlidis/bmc/bdnf.
Differential expression of BDNF across subsets
Since the study was based on analyzing subsets defined by
experimental conditions (gender, age, disease state etc) it
was of biological interest to examine if BDNF is differentially expressed across subsets within a dataset. We used
Kruskal-Wallis test [23] to measure differential expression. The results of this analysis are given in the Additional files 4, 5 and 6: Differential expression of the BDNF
gene in human, mouse and rat datasets.
Co-expression analysis
Since the expression of BDNF alternative transcripts is tissue-specific and responds to the variety of stimuli, seeking
for correlated genes in each subset separately could help
to reveal condition-specific co-expression. The term "subset" in this case must be understood as "a set of samples
under the same condition".

We derived 119 human, 73 mouse and 107 rat subsets
from the corresponding datasets. Pearson correlation
coefficient (PCC) was chosen as a similarity measure since
it is one of the most commonly used, with many publications describing analysis of Affymetrix platforms
[13,24,25]. PCC between BDNF and other genes' probe
sets was measured across samples for each subset separately. From each subset, probe sets with PCC r > 0.6 were
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Download Affymetrix microarray datasets:
Human, mouse and rat from GEO
~ 30 000 probe sets per platform
Check datasets for BDNF expression:
BDNF probe set presence on the platform
BDNF CALL = PRESENT in > 70% of samples

Non−specific filtering of data in each dataset:
Exclude genes with missing values in > 1/3 of samples
Column−average imputation
Two−fold expression change from the
average in > 5 samples

Dividing datasets into subsets

Co−expression analysis
Pearson correlation coefficient − resampling
~ 9 000 BDNF−correlated genes per species
Co−expression link confirmation

BDNF−correlated 3+ genes
~ 2400 in human
~ 1800 in mouse
~ 740 in rat

Co−expression conservation analysis:
BDNF−correlated genes in human − mouse − rat
~80 conserved BDNF−correlated genes
Discovery of over−represented TFBSs in conserved
BDNF−correlated genes; DiRE and CONFAC

Novel potential regulators of BDNF expression

Figure 1
Microarray data analysis flowchart
Microarray data analysis flowchart. Altogether, 80 human, mouse and rat Affymetrix datasets were analyzed (dataset
selection criteria: > 16 samples per dataset; BDNF detection call PRESENT in more than 70% of the samples). Data was subjected to non-specific filtering (missing values and 2-fold change filtering). Thereafter, datasets were divided into 299 corresponding subsets. Co-expression analysis in human, mouse and rat subsets allowed the detection of genes that co-expressed
with BDNF in more than 3 subsets (~1000 genes for each species). As a result of co-expression conservation analysis, 84 genes
were found to be correlated with BDNF in all three species. Discovery of over-represented motifs in the regulatory regions of
these genes and in BDNF suggested novel regulators of BDNF gene expression.

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selected. It was demonstrated by Elo and colleagues [22]
that in the analysis of simulated datasets a cutoff value r =
0.6 showed both high reproducibility (~0.6 for profile
length equal to 10) and low error. A "data-driven cutoff
value" approach has been rejected because it is based on
the connectivity of the whole network, whereas we
focused only on the links between BDNF and other genes.
A lower threshold of 0.4 generated a list of genes that
showed no significant similarities when analyzed using
g:Profiler tool that retrieves most significant GO terms,
KEGG and REACTOME pathways, and TRANSFAC motifs
for a user-specified group of genes [26]. The value r = 0.6
was chosen over more stringent PCC values because the
lengths of the expression profiles were not too short
(mean profile length ~17, standard deviation ~12). Moreover, the PCC threshold higher than 0.6 was not justified
since we performed further filtering by selecting only conserved correlated genes, thus controlling the spurious
results.
Each probe set correlation with BDNF that passed the
threshold was defined as a "link". It has been previously
shown that a link must be confirmed in at least 3 experiments (3+ link) in order to be called reliable [15]. Therefore, we selected (3+) genes for evolutionary conservation
analysis, narrowing the list of correlated genes to eliminate the noise. g:Profiler analysis of these genes revealed
that the results are statistically significant (low p-values)
and the genes belong to GO categories that are relevant to
biological functions of BDNF. For example, the list of
human genes produced the following results when analyzed with g:Profiler (p-values for the GO categories are
given in the parenthess): nervous system development
(5.96·10-21), central nervous system development
(3.29·10-07), synaptic transmission (4.40·10-11), generation of neurons (1.58·10-08), neuron differentiation
(1.02·10-06), neurite development (4.11·10-07), heart
development (1.67·10-09), blood vessel development
(5.51·10-14), regulation of angiogenesis (7.16·10-09),
response to wounding (1.32·10-11), muscle development
(1.53·10-10), regulation of apoptosis (1.65·10-07), etc.
We have used r = 0.6 as a "hard" threshold value for the
CC. A disadvantage of this approach is that there will be
no connection between BDNF and other genes whose correlation with BDNF is 0.59 in a specific dataset [27]. Using
multiple datasets was expected to remedy this effect. An
alternative approach would be to use "soft" threshold
approaches [27]. According to the soft threshold
approach, a weight between 0 and 1 is assigned to the connection between each pair of genes (or nodes in a graph).
Often, the weight between the nodes A and B is represented by some power of the CC between A and B. However, other similarity measures may be used given that
they are restricted in [0, 1]. A drawback of the weighted
CC approach is that it is not clear how to define nodes that

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are directly linked to a specific node [27] because the
available information is related only to how strongly two
nodes are connected. Thus, if neighbors to a node are
requested, threshold should be applied to the connection
strengths. Alternatively, Li and Horvath [28] have developed an approach to answer this question based on
extending the topological overlap measure (TOM), which
means that the nodes (e.g. genes) should be strongly connected and belong to the same group of nodes. However,
this analysis requires the whole network of a set of genes.
In the current analysis, we did not construct the co-expression network for all the genes of microarray experiments.
Instead, we focused on a small part of it i.e. the BDNF
gene and the genes linked to BDNF. Therefore, TOM analysis was not possible using our approach.
To see how the "weighted CC" method would affect the
results of our study we used a simplified approach.
Instead of applying "hard" threshold (0.6) for the CC we
measured the strength of all the connections between
BDNF and all the genes in a microarray experiment. The
connection strength sj = [(1 + CCj)/2]b, where CCj denotes
the CC between BDNF and the gene j, is between 0 and 1
and b is an integer. In order to define b, analysis of the
scale-free properties of the network is required. However,
we used the value 6. Great b values give lower weight to
weak connections. Then we calculated the average
sj(ave(sj)) among all the subsets. Finally, we sorted the
genes based on their ave(sj) and calculated the overlap of
the top of this list with our results for each species (human
mouse and rat). When restricting the top of the weighted
CC list to the same number of genes that we have
obtained for the 3+ list for each species, we observed that
the top-weighted CC genes overlap extensively with the 3+
list (overlapping > 80%) for each species. Therefore, even
though the "soft" and "hard" thresholding approaches are
considerably different we observe quite extensive overlap
of the results. We would like to stress that we did not
apply the full weighted CC and TOM methodology since
it would require the construction of the whole network
which was beyond the aims of our study. However, such
investigation of the whole co-expression network could
contribute to the understanding of BDNF regulation and
function.
Correlation conservation and g:Profiler analysis
Co-expression that is conserved between phylogenetically
distant species may reveal functional gene associations
[29]. We searched for common genes in the lists of 2436
human, 1824 mouse and 740 rat genes (3+ genes, whose
expression is correlated with BDNF). From these genes,
490 were found to be correlated with BDNF in human
and mouse, 210 correlated with BDNF in human and rat,
and 207 conserved between mouse and rat [see Additional file 7: Conserved BDNF-correlated genes]. We
found a total of 84 genes whose co-expression with BDNF
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GO category

Conserved correlated genes

protein tyrosine
kinase PW *

ANGPT1

BAIAP2

PTPRF

FP106

dendrite
localization*

DBN1

signal
transduction*

DUSP1

EPHA4

EPHA5

EPHA7

FGFR1

GAS6

KALRN

IRS2

NTRK2

FREQ

GRIA3

KCND2

NTRK2

ANGPT1

CREM

DUSP6

EPHA5

FGFR1

IGFBP5

KALRN

NR4A2

PDE4B

PRKAG2

PTPRF

TBX3

BAIAP2
COL11A1

CXCL5
DUSP1

EGR1
EPHA4

EPHA7
FGF13

GAS6
GRIA3

IL6ST
IRS2

KLF10
MYH9

NTRK2
ODZ2

PENK
PLAUR

PRKCB
PRKCE

RGS4
SCG2

ZFP106

hsa-miR-369-3p*

COL11A1

DBC1

DCN

DUSP1

GAS6

ITF-2

KLF10

NEUROD6

PENK

TRPC4

TF:
CCCGCCCCCR
CCCC (KROX) *

ATF3

ATP1B1

CCND2

COL11A1

DBN1

DLGAP4

EPHA7

GAS6

GRIA3

IL6ST

IRS2
KCND2

KLF10

NFIA

NPTXR

PCSK2

SNCA

THRA

ATF3

CCND2

DBC1

DUSP6

FREQ

ITF-2

MBP

NPTXR

PCSK1

PTGS2

THRA

BAIAP2
BASP1
CAMK2D

COL4A5
CREM
CXCL5

DBN1
DLGAP4
DUSP1

EGR1
EPHA5
EPHA7

GRIA3
HN1
IRS2

KALRN
KLF10
LMO7

MDM2
NFIA
NPTX1

NR4A2
NTRK2
OLFM1

PDE4B
PRKCB1
PRSS23

PTPRF
PURA
TBX3

TRPC4
VCAN

NS development*

BAIAP2
DBN1

EPHA4
EPHA7

FGF13
FGFR1

IRS2
KALRN

MBP
NEFL

NEUROD6
NPTX1

NR4A2
NTRK2

OLFM1
PCSK2

PTPRF
PURA

SMARCA4
SNCA

TBX3

angiogenesis

ANGPT1

BAIAP2

CYR61

MYH9

SCG2

SERPINE1

TBX3

apoptosis/
anti-apoptosis

BIRC4

KLF10

NEFL

PLAGL1

PRKCE

SCG2

SNCA

cell cycle

CAMK2D

CORO1A

DUSP1

MDM2

MYH9

PPP3CA

synaptic
transmission/
plasticity

DBN1

KCND2

MBP

NPTX1

NR4A2

SNCA

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TF:
GGGGAGGG
(MAZ/SP1) *

TBX3

GO categories marked with a star (*) have been reported as statistically significant for this gene list by g:Profiler analysis tool. Human gene names are given representing mouse and rat orthologs
whenever gene names for all three species are not the same. GO - gene ontology, PW - pathway, TF - transcription factor, NS - nervous system.

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Table 1: BDNF-correlated genes conserved between human, mouse and rat.

BMC Genomics 2009, 10:420

was conserved in all three organisms (Table 1) [see also
Additional file 7: Conserved BDNF-correlated genes].
Due to a variety of reasons (e.g. sample size of a dataset/
subset, probe set binding characteristics, sample preparation methods, etc.), when measured only in one dataset/
subset, some of the co-expression links might occur by
chance. Checking for multiple re-occurrence of a link is
expected to reduce the number of false-positive links.
More importantly, the conservation analysis should further reduce the number of artifacts. However, since our
analysis comprised a multitude of subsets it was important to estimate the statistical significance of the results.
To tackle this problem, we created randomized subsets
similarly to what was described by Lee and colleagues [15]
and calculated the distribution of correlated 3+ links for
each species separately. The results showed that our coexpression link confirmation analysis resulted in a significantly higher number of links compared to the randomized data (p-value < 0.005 for each species). However,
it should be mentioned that the number of 3+ links
remained quite high in the randomized datasets: for
human subsets it constituted about 58% of the observed
3+ links, for mouse about 43% and for rat 21%. These
results justify the subsequent co-expression conservation
analysis step. Indeed, in random human, mouse and rat
subsets the number of correlated 3+ links was only about
9% of the discovered conserved BDNF-correlated links
(that is ~7.5 genes out of 84).
Analysis of the list of 84 conserved BDNF-correlated genes
using g:Profiler showed significantly low p-values for all
the genes and revealed significant GO categories related to
BDNF actions [see Additional file 8: g:Profiler analysis].
Statistically significant GO categories included: i) MYCassociated zinc finger protein (MAZ) targets (44 genes, p =
1.82·10-05); ii) signal transduction (36 genes, p =
3.51·10-06); iii) nervous system development (17 genes, p
= 5.27·10-08); iv) Kruppel-box protein homolog (KROX)
targets (18 genes, p = 1.21·10-04); v) transmembrane
receptor protein tyrosine kinase pathway (7 genes, p =
3.56·10-06); vi) dendrite localization (5 genes, p =
1.82·10-05) (Table 1).
According to the Gene Ontology database, conserved
BDNF-correlated gene products participate in axonogenesis (BAIAP2), dendrite development (DBN1), synaptic
plasticity and synaptic transmission (DBN1, KCND2,
MBP, NPTX1, NR4A2 and SNCA), regeneration (GAS6,
PLAUR), regulation of apoptosis (XIAP (known as
BIRC4), KLF10, NEFL, PLAGL1, PRKCE, SCG2, SNCA, and
TBX3), skeletal muscle development (MYH9, PPP3CA,
and TBX3) and angiogenesis (ANGPT1, BAIAP2, CYR61,
MYH9, SCG2, SERPINE1 and TBX3) (Table 1). Out of 84,
24 BDNF-correlated genes are related to cancer and 14 are
involved in neurological disorders (Table 2).

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Interactions among correlated genes
We searched if any of the correlated genes had known
interactions with BDNF using Information Hyperlinked
over Proteins gene network (iHOP). iHOP allows navigating the literature cited in PubMed and gives as an output
all sentences that connect gene A and gene B with a verb
http://www.ihop-net.org/[30]. We constructed a "gene
network" using the iHOP Gene Model tool to verify
BDNF-co-expression links with the experimental evidences reported in the literature (Figure 2). For the URL
links to the cited literature see Additional file 9: iHOP references.

According to the literature, 17 out of 84 conserved correlated genes have been reported to have functional interaction or co-regulation with BDNF (Figure 2A). IGFBP5
[31], NR4A2, RGS4 [32] and DUSP1 [33] have been previously reported to be co-expressed with human or rodent
BDNF. Other gene products, such as FGFR1 [34] and
SNCA [35] are known to regulate BDNF expression. Proprotein convertase PCSK1 is implied in processing of proBDNF [36]. PTPRF tyrosine phosphatase receptor associates with NTRK2 and modulates neurotrophic signaling
pathways [37]. Thyroid hormone receptor alpha (THRA)
induces expression of BDNF receptor NTRK2 [38]. Finally,
expression of such genes like EGR1 [39], MBP [40], NEFL
[41], NPTX1 [42], NTRK2, SERPINE1 [43], SCG2 [44],
SNCA [45] and TCF4 (also known as ITF2) [46] is known
to be regulated by BDNF signaling. CCND2, DUSP1,
DUSP6, EGR1 and RGS4 gene expression is altered in cortical GABA neurons in the absence of BDNF [47].
iHOP reports the total of 250 interactions with human
BDNF. In order to assess the probability of observing 17/
84 or more functional interactions between BDNF and
other genes, we had to make an assumption regarding the
total number of human genes that iHOP uses. A lower
number of total genes would result in higher p-values
whereas a higher number of total genes would produce
lower p-values. We assumed that the total number of
human genes is N = 5000, 10000, 20000 or 30000. Furthermore, the total number of genes linked to BDNF is m
= 250 based on iHOP data. Thus, the p-values were
obtained using the right-tail of the hypergeometric probability distribution. For N = 5000, 10000, 20000 or 30000,
the p-values are 1.0 × 10-07, 1.7 × 10-12, 1.3 × 10-17, 1.18 ×
10-20 respectively.
By analyzing the iHOP network indirect connections with
BDNF could be established for the genes that did not have
known direct interactions with BDNF (Figure 2B). For
example, SCG2 protein is found in neuroendocrine vesicles and is cleaved by PCSK1 [48] - protease that cleaves
pro-BDNF. BDNF and NTRK2 signaling affect SNCA gene
expression and alpha-synuclein deposition in substantia
nigra [49]. ATF3 gene is regulated by EGR1 [50], which
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Table 2: Conserved correlated genes are associated with various types of cancer and neurological disorders.

Disease

Associated genes

References

Schizophrenia

BDNF RGS4 NR4A2

Schmidt-Kastner et al. (2006)

Parkinson's disease

BDNF PTGS2 SNCA NR4A2

Murer et al. (2001)
Chae et al. (2008)
Pardo and van Duijn (2005)

Alzheimer's

BDNF
KALRN

Murer et al. (2001)
Youn et al. (2007)

Polyglutamine neurodegeneration

NEFL
BAIAP2

Mosaheb et al. (2005)
Thomas et al. (2001)

alpha-mannosidosis

MAN1A1

D'Hooge et al. (2005)

Ophthalmopathy

CYR61 DUSP1 EGR1 PTGS2

Lantz et al. (2005)

Epilepsy

BDNF DUSP6 EGR1

Binder and Scharfman (2004)
Rakhade et al. (2007)

Depression

BDNF DUSP1

Russo-Neustadt and Chen (2005)
Rakhade et al. (2007)

Ischemia

BDNF CD44 PTGS2

Binder and Scharfman (2004)
Murphy et al. (2005)

Ovarian carcinoma

BDNF ITF2 DUSP1 RGS4

Yu et al. (2008)
Kolligs et al. (2002)
Puiffe et al. (2007)

Breast cancer

BDNF FGFR1 CCND2 PLAU SERPINE1 PLAUR MAZ DUSP6
EGR1
KFL10
PTRF

Tozlu et al. (2006)
Koziczak et al. (2004)
Grebenchtchikov et al. (2005)
Cui et al. (2006)
Liu et al. (2007)
Reinholz et al. (2004)
Levea et al. (2000)

Lung cancer

BDNF ODZ2 CCND2 GFI1

Ricci et al. (2005)
Kan et al. (2006)

Prostate cancer

BDNF IGFBP5 PLAUR p75NTR

Bronzetti et al. (2008)
Nalbandian et al. (2005)

Pheochromocytoma

PCSK1 PCSK2 SCG2

Guillemot et al. (2006)

Endometrial cancer

CXCL5 OLFM1

Wong et al. (2007)

Leukemia

PKCB1 CCND2

Hans et al. (2005)

expression is activated by BDNF [39]. For more interactions see Figure 2.
Motif discovery
Assuming that genes with similar tissue-specific expression patterns are likely to share common regulatory elements, we clustered co-expressed genes according to their
tissue-specific expression using information provided by
TiProd database [51]. Each tissue was assigned a category

and the genes expressed in corresponding tissues were
clustered into the following categories: i) CNS, ii) peripheral NS (PNS), ii) endocrine, iii) gastrointestinal, and iv)
genitourinary. We applied DiRE [52] and CONFAC [53]
motif-discovery tools to search for statistically over-represented TFBSs in the clusters and among all conserved
BDNF-correlated genes. DiRE can detect regulatory elements outside of proximal promoter regions, as it takes
advantage of the full gene locus to conduct the search. The
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A

B
IGFBP5

Nptx1

PTPRF
CXL5

PLAUR

PCSK2

PTPRF

RGS4

SERPINE1

OLFM1

Pcsk1

Dusp1

DBC1

KLF10

MBP
BDNF
NTRK2

TCF4

EGR1

ATF3

NTRK2
FGFR1

CCND2

ODZ2

DUSP1

CYR61

MDM2
FGFR1

PRKCB1

EGR1

SMARCA4

NR4A2

ANGPT1

DUSP6

NR4A2

RGS4
SCG2

Serpine1
DCN

SNSCA

CREM
CD44
MBP

Scg2

PCSK1

Nefl

PURA

PENK

IGFBP5

PTGS2

NEFLS

SNCA

IL6ST

Figure 2
Reported interactions between conserved correlated genes in human
Reported interactions between conserved correlated genes in human. Connections between the genes were created
by accessing the literature using iHOP tool. (A) Interactions between correlated genes and BDNF. Arrows: "↔" co-expression
or co-regulation; "BDNF← "regulation of BDNF; "BDNF→" regulation by BDNF. (B) Connections among correlated genes.

software predicts function-specific regulatory elements
(REs) consisting of clusters of specifically associated and
conserved TFBSs, and it also scores the association of individual TFs with the biological function shared by the
group of input genes [52]. DiRE selects a set of candidate
REs from the gene loci based on the inter-species conservation pattern which is available in the form of precomputed alignments of genomic sequence from fish, rodent,
human and other vertebrate lineages [54]. This type of the
alignment enables the tool to detect regulatory elements
that are phylogenetically conserved at the same genomic
positions in different species. CONFAC software [53] enables the identification of conserved enriched TFBSs in the
regulatory regions of sets of genes. To perform the search,
human and mouse genomic sequences from orthologous
gene pairs are compared by pairwise BLAST, and only significantly conserved (e-value < 0.001) regions are analyzed for TFBSs.
Using DiRE we discovered two regulatory regions at the
human BDNF locus that were enriched in TFBSs (Figure 3)
[see also Additional file 10: DiRE motif discovery results
for BDNF and 84 conserved correlated genes]. The first
regulatory region spans 218 bp and is located 622 bp
upstream of human BDNF exon I transcription start site
(TSS). The second putative regulatory region is 1625 bp
long and located 2915 bp downstream of the BDNF stopcodon. Analysis of mouse and rat gene lists produced similar results. Significant over-representation of binding
sites for WT1, KROX, ZNF219, NFkB, SOX, CREB, OCT,
MYOD and MEF2 transcription factors was reported by
DiRE in BDNF and BDNF-correlated genes when all the
genes were analyzed as one cluster [see Additional file 10:
DiRE motif discovery results for BDNF and 84 conserved
correlated genes]. Also, the following cluster-specific overrepresentation of TFBSs was detected: i) CNS - KROX; ii)

endocrine - TAL1beta/TCF4, ETS2, SOX5, and ARID5B
(known as MRF2); iii) gastrointestinal - MMEF2, and
SREBF1; iv) genitourinary - ATF4/CREB, and GTF3 (TFIII)
(Table 3) [see also Additional file 11: DiRE motif discovery results for conserved BDNF-correlated genes clustered
by tissue-specific expression].
To cross-check the results obtained with DiRE, we
repeated the analysis using the CONFAC tool. CONFAC
results overlapped with DiRE results and suggested novel
regulatory elements in human BDNF promoters/exons IIX and in BDNF 3'UTR, which were highly conserved
among mammals and over-represented in the BDNF-correlated genes. Then, evolutionary conservation across
mammals was checked for the core element of each TFBS
discovered in the BDNF gene using UCSC Genome
Browser. Based on MW test results [see Additional file 12:
The results of Mann-Whitney tests (CONFAC)], on the
Importance score [see Additional file 10: DiRE motif discovery results for BDNF and 84 conserved correlated
genes] and on the conservation data (UCSC), we propose
potential regulators of BDNF (Figure 3 and Table 3) [see
also Additional file 13: Highly conserved TFBSs in the
BDNF gene (according to DiRE and CONFAC)]. It is
remarkable, that the TFBSs discovered in the BDNF gene
are highly conserved: most of the TFBSs are 100% conserved across mammals from human to armadillo, some
of them being conserved even in fish (Figure 3).

Discussion
Microarray meta-analysis has proved to be useful for constructing large gene-interaction networks and inferring
evolutionarily conserved pathways. However, it is rarely
used to explore the regulatory mechanisms of a single
gene. We have exploited microarray data from 80 experiments for the purpose of the detailed analysis of the conPage 9 of 20
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IK1 IK1

exon

I

exon

MAZ
IK1

exon
III

II

Mammal Cons
Chimp
Rhesus
Mouse
Rat
Dog
Cat
Cow
Armadillo
Opossum
Chicken
X_tropicalis
Zebrafish
Tetraodon
Fugu

27699500

MEF2 OCT1

exon exon Vh
V

exon IV

27680500

27680000

27679500

human chr:11

2769850
0

CREB

exon VI

27679000

exon
Mammal Cons
VII

27678500

27678000

Chimp
Rhesus
Mouse
Rat
Dog
Cat
Cow
Armadillo
Opossum
Chicken
X_tropicalis
Zebrafish
Tetraodon
Fugu
human chr:11

exon VIII Mammal Cons

27652500

2763750
0

27637000

2763650
0

Chimp
Rhesus
Mouse
Rat
Dog
Cat
Cow
Armadillo
Opossum
Chicken
X_tropicalis
Zebrafish
Tetraodon
Fugu
human chr:11

FO
X
BR O4
N2 ME
NF F2 O
GA
k
C
SOTA1 B T1
X5
MR
FO
F2
XO
4B
XF
D2 RN
AR 2
NT
GA
TA
1
GA
TA
1
OC
T1
GA
GF
T
I
IK 1 S8 A1
1
TA BRN2
GALB/I
TA TF
FO 1
2
XO ME
4
XF F2
D2

GATA1

27638000

GATA1

27638500

ME
F2
SO S8
X
OC 5 M
T1 EF
2
S8 FOXO
ME BRN 4
F2 2
OC SOX
IK
T1 5
1
GA
TA
TA
1
LB
/I
TF
2

27639000

Mammal Cons

BDNF promoter IX

proBDNF

exon IXabc

27639500

FO
X
MR O4
F2
S8
GA
IKTA1
GA 1
TA
1

2765300
0

MY
OD
MY
OD

27653500

FO
X
BR O4
N2
S8

27654000

Chimp
Rhesus
Mouse
Rat
Dog
Cat
Cow
Armadillo
Opossum
Chicken
X_tropicalis
Zebrafish
Tetraodon
Fugu
human chr:11

BDNF promoter VIII

ET
S2
OC S8
T1
GA
TA
1
FO
XO
4

27681000

27699000

GF
SR I1
EB
P1

27700000

KR
O
ME X
GAF2 B
R
T
IK A1 G N2
FI
MA1
1
Z

27700500

TA
L
GA B/IT
T
GF A1 F2
B
I
ME 1S8 RN2
IKF2 C EGR
1 R
2
GA MAZ EB
T
S8 A1 USF
OC BRN
T1 2

27701000

BDNF cluster II: promoters IV-VII

ARNT
NMYC
WT1
USF

FO
X
TA O4
L
S8 B/I
OC TF2
T1
S8
GF
OC I1
T1
S8
G
ME FI1
F2
BR
N
US 2 S8
GAF NM
TA YC
NR 1 B
SF RN
2
ME
F2
FO
XO
4O
CT
ME
1
F2
GA GATA
TA
1
OC 1
T1
GF
BR
I
N
FO 2 S8 1
XO
4
S8
GF
I1

NFkB
IK1

BDNF cluster I: promoters I-III

http://www.biomedcentral.com/1471-2164/10/420

CR
EB
P1
MR
F2
MY GAT
A1
OD
IK
1
GF
I1
CR
EB
CH
P1
OP

BMC Genomics 2009, 10:420

27635500

27635000

27634500

27634000

27633500

27633000

27632500

Chimp
Rhesus
Mouse
Rat
Dog
Cat
Cow
Armadillo
Opossum
Chicken
X_tropicalis
Zebrafish
Tetraodon
Fugu
human chr:11

BDNF 3’UTR

Mammal Cons

Figure 3
Novel regulatory elements in the BDNF gene
Novel regulatory elements in the BDNF gene. Highly conserved TFBSs in the BDNF locus as predicted by DiRE and
CONFAC tools. Given TFBSs were also found to be over-represented in the BDNF-correlated genes. Histograms represent
evolutionary conservation across 9 mammal species (adapted from UCSC Genome Browser at http://genome.ucsc.edu/) (39).
The height of the histogram reflects the size of the conservation score. Conservation for each species is shown in grayscale
using darker values to indicate higher levels of overall conservation. Missing sequences are highlighted by regions of yellow. Single line: no bases in the aligned species; double line: aligning species has one or more unalignable bases in the gap region. Transcribed regions (BDNF exons and 3'UTR) are highlighted in green; non-transcribed regions (BDNF promoters and introns) are
highlighted in blue. Red ovals represent TFBSs mapped to the BDNF gene sequences. Mapped TFBSs have Matrix Similarity
score >0.85 and Core Similarity score >0.99. Core elements of presented TFBSs have 100% of conservation across mammals.
For the structure of human BDNF see Pruunsild et al., 2007 [11].

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Table 3: Over-represented conserved TFBSs in human BDNF and in the BDNF-correlated genes as predicted by DiRE and CONFAC.

TFBS

p-value
Target genes
CONFAC

ARNT

0.012

BDNF pI-II, BDNF 3'UTR; PRKCE, USP2, CAMK2D, CCND2, NEUROD6, THRA, DUSP1, CBX6, ATP1B1, FREQ, ITF-2

POU3F2
(BRN2)

< 0.001

BDNF pII-V, BDNF exon II, IV, IX, BDNF3'UTR; USP2, CAMK2D, THRA, NFIA, PRSS23, CBX6, CUGBP2, EPHA5, EPHA7,
BAIAP2, RKCE, CPD, EPHA4, IL6ST, CCND2, DUSP6, KCND2, MAN1A1, SCG2, GRIA3, COL11A1, TRPC4, FGF13, HN1,
ANGPT1, TCF4, MYH9, PCSK1

CHOP

NA

BDNF I, COL11A1, CD44, BAIAP2, PPP3CA, IL6ST, NEUROD6, SCG2, CYR61, IGFBP5, THRA, NFIA, FGF13, ATP1A2,
ANGPT1, DBC1, CUGBP2, EGR1

CREB

0.013

BDNF pI, IV, VI, BDNF exon I; BAIAP2, PRKCE, USP2, EPHA4, CAMK2D, CCND2, FGFR1, CYR61, GRIA3, THRA, DUSP1,
PENK, PCSK1, PCSK2, HN1, ATP1B1, EGR1, COL4A5, KLF10, EPHA4, FGF13, CBX6, CUGBP2, EPHA5

ETS2

NA

BDNF pII, VIII; THRA, EPHA7, FGF13, BAIAP2 and NFIA promoters, and in COL11A1, PLAGL1, and XIAP intergenic regions

FOXO4

< 0.001

BDNF exon I, II, VIII, IX, BDNF pIII, IV, BDNF 3'UTR; CD44, TBX3, BAIAP2, PPP3CA, CPD, USP2, PRKCB, EPHA4, CORO1A,
CAMK2D, NEUROD6, FGFR1, SCG2, CYR61, GRIA3, THRA, NFIA, COL11A1, DUSP1, TRPC4, PRSS23, PCSK2, ANGPT1,
FREQ, PRKAG2, TCF4, MYH9, PCSK1, DBC1, CUGBP2, EGR1, EPHA5

GATA1

< 0.001

BDNF pI, III-V, BDNF exon I, II, VIII, IX, BDNF 3'UTR; CD44, TBX3, SNCA, PPP3CA, PRKCE, COL4A5, USP2, EPHA4, IL6ST,
SLC4A7, CAMK2D, ATF3, CCND2, NEUROD6, DUSP6, KCND2, SCG2, CYR61, IGFBP5, THRA, NFIA, COL11A1, PENK,
FGF13, PRSS23, ATP1B1, ATP1A2, ANGPT1, DBC1, CUGBP2, EGR1

GFI1

< 0.001

BDNF exon I, BDNF pII-VI, BDNF 3'UTR; SNCA, ATP1A2, MYH9, DBC1, CD44, BAIAP2, PPP3CA, PRKCE, COL4A5, CPD,
USP2, EPHA4, IL6ST, SLC4A7, CAMK2D, CCND2, NEUROD6, KCND2, SCG2, CYR61, IGFBP5, THRA, NFIA, COL11A1,
DUSP1, TRPC4, PENK, FGF13, PRSS23, PCSK2, ATP1B1, PTPRF, ANGPT1, TCF4, CUGBP2, EGR1, EPHA5, EPHA7

IK1 (ikaros)

< 0.001

BDNF pI, BDNF exon I-V, IX, BDNF 3'UTR; PRKCB, KLF10, KCND2, THRA, NFIA, COL11A1, FGF13, ATP1A2, MYH9, PCSK1,
CUGBP2, EPHA7

KROX family NA

BDNF pV, BDNF exon IV; PPP3CA, NFIA, DBN1, KCND2, IRS2, MAN1A2, CCND2, PVRL3, XIAP, DLGAP4, CYR61, ATP1B1,
PURA, SMARCA4, MYH9, GRIA3, EPHA4, DUSP6, EGR1, COL4A5, TRPC4, PRKCB, NPTX1, PTGS2, EPHA5, FGFR1, CBX6,
PRKCE, KLF10, THRA, ATP1A2, BAIAP2, CPD, CORO1A, CAMK2D, IGFBP5, DUSP1, PTPRF, FREQ, PRKAG2

MAZ

NA

BDNF pVh, BDNF exon III, IV; CD44, PPP3CA, PRKCE, COL4A5, USP2, PRKCB, KLF10, EPHA4, CAMK2D, CCND2, DUSP6,
GRIA3, THRA, COL11A1, PENK, FGF13, CBX6, ATP1B1, PTPRF, ATP1A2, FREQ, DBN1, CUGBP2, EGR1, EPHA7

MEF2

NA

BDNF pII-V, BDNF exon II, IX, BDNF 3'UTR; CD44, TBX3, BAIAP2, PPP3CA, PRKCE, COL4A5, EPHA4, IL6ST, CAMK2D,
CCND2, NEUROD6, DUSP6, MAN1A1, IGFBP5, COL11A1, TRPC4, PRSS23, ANGPT1, FREQ, PURA, MYH9, PCSK1, CUGBP2,
EPHA7, SNCA, FGF13

MYC/MAX

NA

BDNF pI, II, IV; CD44, TBX3, PRKCE, USP2, CAMK2D, CCND2, NEUROD6, THRA, NFIA, DUSP1, CBX6, ATP1B1, FREQ, ITF2, EGR1

MYCN

NA

BDNF pI, II; PRKCE, USP2, CAMK2D, CCND2, NEUROD6, THRA, DUSP1, CBX6, ATP1B1, FREQ, ITF-2

MYOD

< 0.001

BDNF exon I, IX; CD44, PRKCE, USP2, PRKCB, EPHA4, DUSP6, SCG2, SMARCA4, THRA, PRSS23, ATP1B1, CUGBP2

NFkB

< 0.001

BDNFI, BDNF 3'UTR; PPP3CA, KLF10, PCSK2, ATP1B1, ANGPT1, MYH9, USP2, DUSP6, FGF13, PURA, BAIAP2, CAMK2D,
CCND2, FGFR1, CYR61, PCSK2, MYH9, CUGBP2, EGR1, EPHA7

NRSF

NA

BDNFII, EPHA4, IRS2, EPHA5, NPTX1, PRKCB, TRPC4, COL4A5

S8

< 0.001

BDNF pII-IV, BDNF exon II, IV, VIII, IX, BDNF 3'UTR; CD44, BAIAP2, PRKCE, NPTX1, EPHA4, CAMK2D, CCND2, NEUROD6,
DUSP6, FGFR1, KCND2, MAN1A1, SCG2, THRA, NFIA, COL11A1, PENK, PCSK2, ANGPT1, PURA, ITF-2, MYH9, DBC1,
CUGBP2, EGR1, EPHA5

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Table 3: Over-represented conserved TFBSs in human BDNF and in the BDNF-correlated genes as predicted by DiRE and CONFAC.

SOX5

0.001

BDNF exon I, BDNF 3'UTR; EPHA4, THRA and PLAGL1 3'UTR; NFIA and OLFM1 promoters; SCG2 intergenic region; KCND2
intron

TAL1/TCF4

NA

BDNF pIV, BDNF exon I, BDNF 3'UTR; ATP1B1 3'UTR, MYH9 3'UTR and XIAP 3'UTR; SCG2, CD44, SERPINE1, SLC4A7,
CCND2, NEUROD6, FGFR1, THRA, COL11A1, PCSK2, ANGPT1, DBC1, CUGBP2

WT1

NA

BDNF pI, BASP1, PPP3CA, NFIA, DBN1, EPHA7, BAIAP2, XIAP, DLGAP4, PURA, IRS2, ATP1B1, KCND2, GRIA3, HN1, EPHA4,
EGR1, COL4A5, TRPC4, ATP1A2, PRKCB, NPTX1, DBC1, EPHA5

In BDNF, TFBSs were found in promoters (p), exons or 3'UTR of the gene. In the correlated genes, TFBSs were searched for and discovered mostly
in promoters (unless indicated otherwise). P-values are given for the TFBSs discovered using CONFAC. NA - not applicable for the TFBSs
discovered using DiRE [see Additional files 10 and 11 for TFBS importance score].

servation of BDNF gene expression and regulation.
Analysis of co-expression conservation combined with
motif discovery allowed us to predict potential regulators
of BDNF gene expression as well as to propose novel gene
interactions. Several transcription factors that were identified here as potential regulators of human BDNF gene
have been previously shown to regulate rodent BDNF
transcription in vitro and in vivo. These transcription factors include REST (also known as NRSF) for BDNF promoter II [55], CREB for BDNF promoter I and IV [56,57],
USF [58], NFkB [59], and MEF2 for BDNF promoter IV
[60]. The support of the bioinformatics findings by experimental evidence strongly suggests that the potential regulatory elements discovered in this study in the BDNF locus
may be involved in the regulation of BDNF expression.
According to g:Profiler, 44 out of 84 conserved correlated
genes identified in this study (including BDNF) carry
MYC-associated zinc finger protein (MAZ) transcription
factor binding sites. Our study revealed putative binding
sites for MAZ in BDNF promoter Vh and in exons III and
IV, suggesting that MAZ could be involved in BDNF gene
regulation from promoters III, and possibly from promoters IV, V, Vh and VI that lie in close proximity in the
genome. It has been shown that MAZ is a transcriptional
regulator of muscle-specific genes in skeletal and cardiac
myocytes [61]. Histone deacetylation and DNA methylation might be involved in the regulation of expression of
target genes by MAZ [62]. BDNF mRNA expression in the
heart is driven by promoters IV, Vh and VI [11]. Epigenetic
regulation of the BDNF gene expression is achieved in a
cell-type and promoter-specific manner [12,63]. This
could be a possible regulation mechanism of the BDNF
gene by MAZ. Also, MAZ drives tumor-specific expression
of PPARG in breast cancer cells, a nuclear receptor that
plays a pivotal role in breast cancer [64]. Expression levels
of BDNF and BDNF-correlated genes CCND2, DUSP6,
EGR1, KLF10 and PTPRF are altered in breast cancer (see
Table 2). These genes were identified as putative targets of
MAZ in the present study suggesting potential role for
MAZ in their regulation in breast cancer cells.

Our analysis revealed that Wilms' tumor suppressor 1
(WT1) transcription factor binding sites are overrepresented in the BDNF-correlated genes. WT1 binding sites
were detected in BDNF promoter I, in IRS2 (insulin receptor substrate 2), EGR1, BAIAP2 (insulin receptor substrate
p53) and PURA promoters and in 19 other genes. WT1
acts as an oncogene in Wilms' tumor (or nephroblastoma), gliomas [65] and various other human cancers
[66]. WT1 activates the PDGFA gene in desmoplastic
small round-cell tumor, which contributes to the fibrosis
associated with this tumor [67]. Puralpha (PURA), a putative WT1 target gene identified in this study, has also been
reported to enhance transcription of the PDGFA gene
[68]. WT1 regulates the expression of several factors from
the insulin-like growth factor signaling pathway [69].
WT1 was also shown to bind the promoter of EGR1 gene
[70]. Neurotrophins and their receptors also may be
involved in the pathogenesis of some Wilms' tumors [71].
Transcriptional activation of BDNF receptor NTRK2 by
WT1 has been shown to be important for normal vascularization of the developing heart [72]. Moreover, WT1
might have a role in neurodegeneration, observed in
Alzheimer's disease brain [73]. We hypothesize that
BDNF and other WT1 targets identified in this study, can
play a role in normal development and tumorigenesis
associated with WT1.
KROX family transcription factors' binding sites were
found to be abundant in the promoters of BDNF and
BDNF-correlated genes. KROX binding motif was detected
in BDNF promoter V and EGR2 binding site was found in
BDNF promoter IV. Also, EGR1 gene expression was correlated with BDNF in human, mouse and rat. KROX family of zinc finger-containing transcriptional regulators,
also known as Early Growth Response (EGR) gene family,
consists of EGR1-EGR4 brain-specific transcription factors
[74] that are able to bind to the same consensus DNA
sequence (KROX motif) [75]. EGR1 is involved in the
maintenance of long-term potentiation (LTP) and is
required for the consolidation of long-term memory [76].
EGR3 is essential for short-term memory formation [77]
and EGR2 is necessary for Schwann cell differentiation

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BMC Genomics 2009, 10:420

and myelination [78,79]. Since BDNF plays a significant
role in the above mentioned processes, it would be
intriguing to study the regulation of BDNF by EGR factors.
Binding sites for GFI1 and MEF2 were found in BDNF
promoters, exons and 3'UTR, and in the promoter of the
SNCA gene. GFI1 binding sites were detected in BDNF
promoters II-VI and in exon I. MEF2 sites were found in
BDNF promoters II-V and in exons II and IX. SNCA overexpression and gene mutations that lead to SNCA protein
aggregation cause Parkinson's disease (PD) [80]. BDNF
and SNCA expression levels change conversely in the
nigro-striatal dopamine region of the PD brain [80,81].
The myocyte enhancer factor-2 (MEF2) is known to be
necessary for neurogenesis and activity-dependent neuronal survival [82,83]. Inactivation of MEF2 is responsible
for dopaminergic loss in vivo in an MPTP mouse model of
PD [84]. MEF2 recruits transcriptional co-repressor
Cabin1 and class II HDACs to specific DNA sites in a calcium-dependent manner [85]. MEF2 is one of the TFs that
contribute to the activity-dependent BDNF transcription
from promoter IV [60]. The growth factor independence1 (GFI1) transcription factor is essential for the development of neuroendocrine cells, sensory neurons, and
blood. Also, GFI1 acts as an oncogene in human small cell
lung cancer (SCLC), the deadliest neuroendocrine tumor
[86]. GFI1 mediates reversible transcriptional repression
by recruiting the eight 21 corepressor (ETO), histone
deacetylase (HDAC) enzymes and the G9a histone lysine
methyltransferase [87]. It has also been shown that GFI1
Drosophila homolog Senseless interacts with proneural
proteins and functions as a transcriptional co-activator
suggesting that GFI1 also cooperates with bHLH proteins
in several contexts [88]. Our findings are impelling to
explore inverse regulation of BDNF and SNCA genes by
GFI1 and MEF2 in neurons generally and in Parkinson's
disease models in particular.
BDNF promoters II-V and BDNF exons II, IV and IX contain BRN2 (brain-specific homeobox/POU domain
POU3F2) binding sequences. BRN2 is driving expression
of the EGR2 gene - an important factor controlling myelination in Schwann cells [78,79]. BRN2 also activates the
promoter of the Notch ligand Delta1, regulating neurogenesis. It also regulates the division of neural progenitors, as well as differentiation and migration of neurons
[89]. Considering a prominent role of BDNF in myelination and neurogenesis, it is reasonable to hypothesize that
BRN2 fulfills its tasks in part by regulating BDNF gene
expression.
Evidence is emerging that not only proximal promoters,
but also distant elements upstream and downstream from
TSS can regulate transcription [90,91]. We found that

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BDNF 3'UTR contains potential binding sites for TCF4
(also known as ITF2), GFI1, BRN2, NFkB and MEF2.
Finally, we have discovered multiple binding sites in
human BDNF promoters for the transcription factors that
have been shown to participate in neuronal activitydependent transcription of rodent BDNF gene. BDNF promoters I and IV are the most highly induced following
neuronal activation. BDNF promoter I was shown to be
regulated by cAMP-responsive element (CRE) and the
binding sequence for upstream stimulatory factor 1/2
(USF) in response to neuronal activity and elevated calcium levels [92]. Several TFs (USF [58], CREB [57], MEF2
[60], CaRF [93] and MeCP2 [63]) regulate BDNF promoter IV upon calcium influx into neurons. Rat BDNF
promoter II has also shown induction by neuronal activity, though to a lesser extent compared to promoters I and
IV [12,94]. However, calcium responsive elements have
not been yet studied in BDNF promoter II and it was
believed that its induction is regulated by the elements
located in the promoter I. Our analysis of human BDNF
gene detected CREBP1 and USF binding sites in BDNF
promoter I, USF and MEF2 binding sites in promoter II
and USF, MEF2 and CREB binding sites in promoter IV.
We suggest that MEF2 and USF elements might contribute
to BDFN promoter II induction by neuronal activity. In
addition, we have detected conserved TCF4 (ITF2) binding sequences in BDNF promoter IV, and in exon I. It has
been shown that calcium-sensor protein calmodulin can
interact with the DNA binding basic helix-loop-helix
(bHLH) domain of TCF4 inhibiting its transcriptional
activity [95]. Preliminary experimental evidence (Sepp
and Timmusk, unpublished data) suggests that TCF4 transcription factor is involved in the regulation of BDNF
transcription. TCF4 might play in concert with CREB,
MEF2 and other transcription factors to modulate BDNF
levels following neuronal activity.
In our study we performed the analysis of a well-known
gene and it served as a good reference to evaluate the
results of the "subset" approach. However, the "subset"
method coupled with the analysis of evolutionary conservation of co-expression is suitable for studying poorly
annotated genes as well. This approach examines coexpression across a variety of conditions, which helps to
discover novel biological processes and pathways that the
guide-gene and its co-expressed genes are related to. Also,
searching for conserved TFBS modules in co-expressed
genes helps to discover functionally important genomic
regions and this does not require detailed prior knowledge of the guide-gene's structure. However, when
attempting to study less known genes, additional in silico
analysis of genomic sequences using bioinformatics tools
for prediction of promoters, TSSs and exon-intron junc-

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tions would be useful. Also, sequence alignment with coexpressed genes' promoters would be informative.

Conclusion
A major impediment of meta-coexpression analysis is the
differences among experiments. So far, analyzing gene
expression across different microarray platforms remains
a challenge. Discrepancies in the expression measurements among different platforms originate from different
probe sequences used, different number of genes on the
platform, etc. Therefore, in order to obtain reliable results,
we used only one microarray platform type for the analysis. In addition, we introduced a new approach to increase
the accuracy of the analysis: we divided datasets into subsets and sought for correlated genes for each subset,
implying that each subset represents an independent
experimental condition. We have also performed correlation link confirmation among subsets and correlation
conservation analysis to discover functionally related
genes.
One of the limitations of the co-expression conservation
analysis is the fact that it detects only phylogenetically
conserved co-expression events. Human-specific phenomena cannot be captured by this kind of analysis. In
relation to BDNF this means, for example, that regulation
of human BDNF gene by antisense BDNF RNA (BDNFOS
gene) [11,96] could not be studied by co-expression conservation analysis, since BDNFOS gene is not expressed in
rodents [12,97]. Also, co-expression analysis using microarray experiments is limited by the number of genes
included in the microarray platforms. For example, since
BDNFOS probe sets were absent from microarray platforms, we could not study co-expression, anti-coexpression or differential expression of BDNF and BDNFOS. In
addition, our list of correlated genes did not include all
possible correlation links with BDNF due to the fact that
our analysis was deliberately limited to Affymetrix microarray platforms. Moreover, in our analysis we included
only those experiments that met certain requirements
regarding the BDNF gene expression. However, biologically meaningful results justify our rigorous filtering
approach: correlated genes identified in this study are
known to regulate nervous system development, and are
associated with various types of cancer and neurological
disorders. Also, experimental evidence supports the
hypothesis, that transcription factor identified here can
act as potential BDNF regulators.
In summary, we have discovered a set of genes whose coexpression with BDNF was conserved between human
and rodents. Also, we detected new potential regulatory
elements in BDNF-correlated genes and in the BDNF
locus using bioinformatics analysis, in which BDNF was
playing a role of a guide-gene. The presented concept of
co-expression conservation analysis can be used to study

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the regulation of any other gene of interest. The study provides an example of using high-throughput advancements
in studying single genes and proposes hypotheses that
could be tested using molecular biology techniques.

Methods
Microarray datasets and data filtering
Homo sapiens, Mus Musculus and Rattus Norvegicus microarray datasets were downloaded from (GEO) [98]. We
selected Affymetrix GeneChips experiments that comprised a minimum of 16 samples. Datasets which contained BDNF Detection call = Absent [99] in more than
30% of the samples were not selected [see Additional file
2: Microarray datasets] for the list of datasets used in the
analysis. Since the arrays contained normalized data, no
additional transformation was performed. To reduce the
noise, we carried out non-specific filtering of data in each
dataset. Genes that had missing values in more than 1/3
of the samples of a given dataset were excluded from the
analysis in order to avoid data over-imputation [100]. For
the remaining genes, we followed a column-average
imputation method. Totally, only 0.098% of the gene
expression values were imputed with this approach. Further, we selected the genes whose expression changes were
greater than two-fold from the average (across all samples) in at least five samples in a dataset [19,49]. Additionally, datasets were eliminated from the study if BDNF
probe sets' expression failed to meet the above mentioned
criteria [see Additional file 1: BDNF probe sets]. Out of 72
human datasets, only 38 passed non-specific filtering,
whereas 24 out of 82 mouse and 18 out of 35 rat datasets
passed the filtering and were used for the analysis.

Each dataset was split into subsets (i.e. normal tissue, disease tissue, control, treatment, disease progression, age,
etc.) so that subsets of the same dataset would not have
any overlapping samples [see Additional file 3: Subsets].
The division into subsets was performed manually,
according to the information included in the experiment.
In some cases subsets could be further subdivided into
biologically appropriate sub-subsets [see Additional file 2:
Microarray datasets and Additional file 3: Subsets]. Subsets that contained less than eight samples were excluded
from analysis to avoid inaccuracy in the estimation of
genic correlations. Biological and technical replicates were
handled as equal. From all human datasets, one (GDS564
dataset) contained one technical replicate per male sample and one technical replicate for all female samples
except one. For the mouse datasets no technical replicates'
data accompanied the dataset information. Finally, in rat
GDS1629 dataset one technical replicate has been used
for each biological replicate.
Differential expression
We used Kruskal-Wallis test [23] to measure differential
expression of BDNF across subsets in each dataset.
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Kruskal-Wallis test is a non-parametric method for testing
equality of population medians within different groups. It
is similar to one-way analysis of variance (ANOVA). However, it does not require the normality assumption. Alternatively, it represents an extension of Mann-Whitney U
test [101,102] for more than 2 samples. Since we used
multiple datasets we applied the false discovery rate
approach (FDR) at the 0.05 level as it is described by Benjamini and Hochberg (1995) [103].
Co-expression analysis
For each gene standard Pearson correlation coefficient
(PCC) was calculated across samples. We followed a resampling strategy, which allows the calculation of the standard deviation of the PCC between a pair of probe sets.
PCC was calculated for each subset separately. The PCC
was calculated following a resampling bootstrap
approach. For example, in order to calculate the CCj
between BDNF and gene j when data consisted of m
points, we resampled the m points with replacement creating 2000 re-samples [104]. Then the CCj was calculated
as the average CC for the 2000 re-samples and the 95%
bootstrap confidence interval was estimated. The average
CC is very close to the sample CC. However, when m is a
small number and outliers are contained in the sample
then the bootstrap confidence interval may be large. The
motivation behind the bootstrap approach is to avoid
genes with large bootstrap confidence intervals. Thus,
when we request the links between BDNF and the genes
in the microarray experiment we ask for the genes j, whose
CCj is greater than 0.6 and the 95% bootstrap confidence
interval contains only positive numbers. If instead of the
bootstrapping approach we would use just the sample CC,
which is more efficient computationally, then a larger set
of links would be obtained which would contain some
genes with very large bootstrap confidence intervals.

A threshold value of r = 0.6 was used to retrieve a list of
probe sets that were co-expressed with the BDNF probe set
[22,49]. Each probe set correlation with BDNF that passed
the threshold was termed as a "link". It should be noted
that the PCC was calculated between probe set pairs and
not between gene-name pairs. Thus, when more than one
probe set-pair was associated with the same gene-pair we
excluded all the links except the one with the highest PCC
value.
Co-expression link confirmation
We defined a "co-expression link confirmation" as a reoccurrence of links in multiple subsets. In order to avoid
artifacts and biologically irrelevant links, we performed
link confirmation to select the genes that were correlated
with BDNF in three or more subsets [15]. It should be
noticed that systematic differential expression within a
subset could result in high PCC values. However, high

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PCC values in this case do not reveal any relationship
between genes and represent a by-product of the differential expression of genes within a heterogeneous subset. We
used a minimum between 1000 and 10% of all the probe
sets within the subset as a threshold. Subsets that yielded
more co-expression links between BDNF and other genes
than an arbitrary threshold were excluded from further
analysis. Thus, 5% of all the subsets were excluded.
Probe set re-annotation and ortholog search
Prior to the identification of the links that are conserved
between human, mouse and rat, we transformed the
probe set-pair links to gene-pair links. We used g:Profiler
[26] to transform the probe set names to Ensemble gene
names (ENSG). However, since many probe sets are currently related to the expressed sequence tags (ESTs), not
all the probe sets could be mapped to the known genes
using g:Profiler. For each dataset, we used its annotation
file (see: ftp://ftp.ncbi.nih.gov/pub/geo/DATA/annota
tion/platforms/). To assign Ensemble gene names to the
"unmapped" probe sets, we obtained the probe set
sequence identifier (GI number) using the annotation file.
Then, we retrieved RefSeq accession for each GI number
from NCBI database. Finally, we continued with a best-hit
blast approach for all three species.
Co-expression conservation and g:Profiler analysis
By performing a co-expression conservation analysis we
identified the links that have passed prior filters (PCC
threshold and link confirmation) and are conserved
among human, mouse and rat.

Genes which co-expression with BDNF was found to be
conserved between human, mouse, and rat constituted
the input list for the g:Profiler. g:Profiler http://
biit.cs.ut.ee/gprofiler/[26] is a public web server used for
characterizing and manipulating gene lists resulting from
mining high-throughput genomic data. It detects geneontology categories that are overrepresented by the input
list of genes or by sorted sublists of the input. g:Profiler is
using the "Set Count and Sizes" (SCS) method to calculate
p-values [26].
Correlated genes' interactions
We used iHOP resource (Information Hyperlinked over
Proteins, http://www.ihop-net.org/) [30] to find reports
in the literature about known interaction between BDNFcorrelated genes. iHOP generates a network of genes and
proteins by mining the abstracts from PubMed. A link in
such a network does not mean a specific regulatory relationship, but any possible interaction between two genes
(such as protein activation, regulation of transcription, coexpression, etc). Each reference was verified manually to
ensure the citation of valid interactions.

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Motif discovery
We clustered BDNF-correlated genes according to their tissue-specific expression using gene expression information
available in the TiProD database [51] (BDNF gene was
included in every cluster). The TiProD database contains
information about promoter tissue-specific expression for
human genes. For each gene the list of tissues where the
gene expression has been detected can be obtained from
TiProD together with the tissue specificity score. For each
gene we extracted information on tissue expression,
selecting tissues with specificity score higher than 0.2.
Each tissue was assigned a category according to its anatomy and function and the genes expressed in corresponding tissues were clustered into CNS, peripheral NS,
endocrine, gastrointestinal or genitourinary cluster. Then,
we searched for combinations of over-represented TFBS
among the list of correlated genes, as well as the tissue
clusters discovered by TiProD.

We used DiRE http://dire.dcode.org/[52] and CONFAC
http://morenolab.whitehead.emory.edu/[53] tools for
the discovery of TFBSs in the conserved co-expressed
genes. DiRE uses position weight matrices (PWM) available from version 10.2 of the TRANSFAC Professional database [105]. In DiRE, up to 5000 background genes can be
used. Only those TFBSs are extracted that occur less frequently in 95% of permutation tests than in the original
distribution (corresponding to a p-value < 0.05 to observe
the original distribution by chance) and that corresponds
to at least a twofold increase in their density in the original
distribution as compared with an average pair density in
permutation tests. To correct for multiple hypothesis testing, the hypergeometric distribution with Bonferroni correction is used in the DiRE tool [106]. For each discovered
TFBS DiRE defines the 'importance score' as the product of
the transcription factor (TF) occurrence (percentage of tissue-specific TF with the particular TFBS) and its weight
(tissue-specificity importance) in a tissue-specific set of
candidate TF. Thus, the importance score is based on the
abundance of the TFBS in tissue-specific TF and on the
specificity of the TF that contain the particular TFBS.
Conserved transcription factor binding site (CONFAC)
software [53] enables the high-throughput identification
of conserved enriched TFBSs in the regulatory regions of
sets of genes using TRANSFAC matrices. CONFAC uses the
Mann-Whitney U-test to compare the query and the background set. It uses a heuristic method for reducing the
number of false positives while retaining likely important
TFBSs by applying the mean-difference cutoff which is
similar to the use of fold change cutoffs in SAM analyses
[107] of DNA microarray data [53]. According to the data
provided by CONFAC, 50 random gene sets were compared to random sets of 250 control genes. Only one TFBS
exceeded 5% false positive rate for the set of 250 random

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control genes that we used in our analysis with the parameters advised by the authors [53]. We used promoter
sequences of BDNF-correlated genes and the sequences of
BDNF promoters, exons, introns and the 3'UTR for the
analysis. Matrix Similarity cut-off 0.85 and Core Similarity
cut-off 0.95 were used for motif discovery; and the parameters recommended by authors - for Mann-Whitney tests
(p-value cutoff 0.05 and mean-difference cutoff 0.5) [53].
Evolutionary conservation across mammals was confirmed manually for the 5-nucleotide core element of each
TFBS discovered in the BDNF gene using UCSC Genome
Browser [108].

Authors' contributions
TA and PP made equal contribution to conception and
design of the study. PP performed computational analysis
of data; TA and TT performed interpretation of the results.
TA and PP were involved in drafting the manuscript; TT
revised the manuscript for important intellectual content.
TA, PP and TT have given final approval of the version to
be published.

Additional material
Additional file 1
BDNF probe sets. Affymetrix microarray probe sets for BDNF gene.
BDNF probe set target sequences are given for each platform type that was
used in the co-expression conservation analysis.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-10-420-S1.xls]

Additional file 2
Microarray datasets. Datasets that passed non-specific filtering and were
used in the analysis (38 human microarray datasets, 24 mouse datasets
and 18 rat datasets). Each dataset was divided into subsets (disease state,
age, agent, etc) according to experimental annotations. When possible,
subsets were subdivided further (marked by *). Experiments were classified based on their description and the tissue origin. GDS refers to GEO
Datasets.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-10-420-S2.xls]

Additional file 3
Subsets. Dataset: GDS1018/1368678_at/Bdnf/Rattus norvegicus.
Expression profiling of brain hippocampal CA1 and CA3 neurons of
Sprague Dawleys subjected to brief preconditioning seizures. According to
the dataset annotation, dataset could be divided into three subsets by cell
type (A) or into two subsets by protocol (B). In addition, subsets could be
subdivided further into cell type.protocol sub-subsets: CA1 pyramidal neuron.control, CA1 pyramidal neuron.preconditioning seizure, CA3 pyramidal neuron.control, etc. After filtering, subset containing less than eight
samples (CA3 pyramidal neuron.preconditioning seizure) was excluded
from the analysis.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-10-420-S3.pdf]

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Additional file 4

Additional file 11

Differential expression of the BDNF gene in human datasets. Differential expression of BDNF was measured across subsets in each dataset
using Kruskal-Wallis test. Only statistically significant results are presented.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-10-420-S4.pdf]

DiRE motif discovery results for conserved BDNF-correlated genes
clustered by tissue-specific expression. TFBSs over-represented in each
tissue cluster are given together with the Importance Score (cut-off 0.1 recommended by DiRE). CNS - central nervous system, PNS - peripheral
nervous system.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-10-420-S11.xls]

Additional file 5

Additional file 12

Differential expression of the BDNF gene in mouse datasets. Differential expression of BDNF was measured across subsets in each dataset using
Kruskal-Wallis test. Only statistically significant results are presented.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-10-420-S5.pdf]

The results of Mann-Whitney tests (CONFAC). Overrepresented TFs in
the conserved BDNF-correlated gene list. Bar graphs show the average
conserved TFBS frequencies for the sample gene set (conserved BDNF-correlated genes, blue bars) and control gene set (random 250 genes, red
bars). A minimum threshold for the differences in the average TFBS frequencies between the two groups was set by p-value cutoff 0.05 and a
mean-difference cutoff 0.5.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-10-420-S12.pdf]

Additional file 6
Differential expression of the BDNF gene in rat datasets. Differential
expression of BDNF was measured across subsets in each dataset using
Kruskal-Wallis test. Only statistically significant results are presented.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-10-420-S6.pdf]

Additional file 13
Highly conserved TFBSs in the BDNF gene (according to DiRE and
CONFAC). Represented TFBSs have Matrix Similarity score >0.85 and
Core Similarity score >0.99. TFBS sequences are highlighted in blue; "+"
or "-" mark the DNA strand orientation; BDNF exons and 3'UTR are
highlighted in green; the regulatory region in BDNF downstream from
polyadenylation sites identified by DiRE is highlighted yellow.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-10-420-S13.htm]

Additional file 7
Conserved BDNF-correlated genes. Genes, whose correlation with
BDNF was confirmed in at least 3 subsets (3+ genes) and was conserved
between i) human, mouse and rat; ii) human and rat; iii) human and
mouse; iv) mouse and rat.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-10-420-S7.xls]

Additional file 8
g:Profiler analysis. Functional profiling of the list of BDNF-correlated
genes conserved between human, mouse and rat using g:G:Profiler. For
details see also http://biit.cs.ut.ee/gprofiler/.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-10-420-S8.txt]

Additional file 9
iHOP references. Interactions between conserved correlated genes in
human and mouse (URL links to the literature cited in iHOP).
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-10-420-S9.xls]

Additional file 10
DiRE motif discovery results for BDNF and 84 conserved correlated
genes. Over-represented TFBSs are given together with the Importance
Score (cut-off 0.1 recommended by DiRE). Numbers 1 and 2 (in All 1
and 2) refer to the different ways that DiRE tool analyzes evolutionary
conserved regions (ECR): 1) top 3 ECRs + promoter ECRs; 2) UTR ECRs
+ promoter ECRs.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-10-420-S10.xls]

Acknowledgements
This work was supported by the following grants: the Wellcome Trust
International Senior Research Fellowship [grant number 067952]; Estonian
Ministry of Education and Research [grant number 0140143]; Estonian
Enterprise [grant number EU27553]; and Estonian Science Foundation
[grant number 7257] to TT; the Volkswagen-Foundation [grant number
824234-1] to PP.
We thank Jüri Reimand and Marko Piirsoo for critical comments on the
manuscript. Mari Sepp, Indrek Koppel, Priit Pruunsild and other members
of our lab are acknowledged for useful suggestions and discussions.

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