HHS Public Access
Author manuscript
Author Manuscript
Arch Toxicol. Author manuscript; available in PMC 2016 May 01.
Published in final edited form as:
Arch Toxicol. 2015 May ; 89(5): 743–755. doi:10.1007/s00204-015-1509-6.
MPTP’s Pathway of Toxicity Indicates Central Role of
Transcription Factor SP1
Alexandra Maertens1, Thomas Luechtefeld1, Andre Kleensang1, and Thomas Hartung1,2,3
Thomas Hartung: THartung@jhsph.edu
1Center
for Alternatives to Animal Testing, Bloomberg School of Public Health, Johns Hopkins
University, Baltimore, MD, USA
Author Manuscript
2Center
for Alternatives to Animal Testing Europe, University of Konstanz, Konstanz, Germany
3Doerenkamp-Zbinden
Professor and Chair for Evidence-Based Toxicology, Bloomberg School of
Public Health, Johns Hopkins University, 615 N. Wolfe St., W7035, Baltimore, MD 21205, USA
Abstract
Author Manuscript
Deriving a Pathway of Toxicity from transcriptomic data remains a challenging task. We explore
the use of weighted gene correlation network analysis (WGCNA) to extract an initial network
from a small microarray study of MPTP toxicity in mice. Five modules were statistically
significant; each module was analyzed for gene signatures in the Chemical and Genetic
Perturbation subset of the Molecular Signatures Database as well as for over-represented
transcription factor binding sites and WGCNA clustered probes by function and captured
pathways relevant to neurodegenerative disorders. The resulting network was analyzed for
transcription factor candidates, which were narrowed down via text-mining for relevance to the
disease model, and then combined with the large-scale interaction FANTOM4 database to
generate a genetic regulatory network. Modules were enriched for transcription factors relevant to
Parkinson’s disease. Transcription factors significantly improved the number of genes that could
be connected in a given component. For each module, the transcription factor that had, by far, the
highest number of interactions was SP1, and it also had substantial experimental evidence of
interactions. This analysis both captures much of the known biology of MPTP toxicity and
suggests several candidates for further study. Furthermore, the analysis strongly suggests that SP1
plays a central role in coordinating the cellular response to MPTP toxicity.
Author Manuscript
Keywords
MPTP; Parkinson’s disease; SP1; WGCNA; GRN; Pathway of toxicity
© Springer-Verlag Berlin Heidelberg 2015
Correspondence to: Thomas Hartung, THartung@jhsph.edu.
Electronic supplementary material The online version of this article (doi:10.1007/s00204-015-1509-6) contains supplementary
material, which is available to authorized users.
Maertens et al.
Page 2
Author Manuscript
Introduction
In order to bring toxicology into the twenty-first century, the field is undergoing a profound
paradigm change: away from animal-based, black-box models toward a systems toxicology
approach based on higher-throughput testing (Hartung 2009). The necessary mapping of
pathways of toxicity as a new emerging concept (Kleensang et al. 2014) often involves using
high-dimensional datasets, which are traditionally analyzed by looking for a few
differentially expressed genes. Cellular pathways leading to toxicity, however, may involve
subtle perturbations in many genes rather than drastic alterations in a few. In addition,
microarrays are intrinsically noisy and often show poor reproducibility, which only
increases the difficulty of extracting meaningful, system-level insights into biology from the
data.
Author Manuscript
The Pathway of Toxicity concept (Hartung and McBride 2011) is at the core of the NIH
Human Toxome project (http://humantoxome.com, Bouhifd et al. 2014). In line, we used an
approach that derives a de novo network from a small dataset, clusters genes into modules
by network topology and uses the resulting modules for further analysis with text-mining
and other sources of high-throughput data (ChIP experiments and siRNA perturbation
studies), ultimately producing a more specific genetic regulatory network (GRN). Using a
WGCNA approach offers, in essence, a dimensionality reduction technique that can be used
to produce a more detailed genetic regulatory network based on known and predicted
transcription factor interactions, bringing us a small step closer to a wiring diagram of the
cell.
Author Manuscript
MPTP (methyl-4-phenyl-1,2,3,6-tetrahydropyridine) toxicity offers an excellent “proof of
concept” for the ability to derive a Pathway of Toxicity from high-throughput data, since the
broad outlines of the Pathway of Toxicity are understood. MPTP exposure is used widely as
an animal model for the relatively data-rich Parkinson’s disease (Schober 2004) since MPTP
poisoning, like Parkinson’s, is highly selective for dopaminergic neurons in the substantia
nigra and the clinical symptoms are highly similar to Parkinson’s (Snyder and D’Amato
1986).
Author Manuscript
MPTP is not itself toxic, but owing to its high lipophilicity it is able to cross the blood–brain
barrier, where it is metabolized in astrocytes by monoamine oxidase B (MOA-B) to MPP+.
MPP+ is then transported selectively by the dopamine transporter into neurons. Once inside
the neuron, it is thought to exert its primary action through targeting Complex I in the
mitochondria, which results in disruption of the electron transport chain (ETC). While
MPTP disruption of the ETC causes a loss of ATP, it is not a critical failure of Complex I
and oxidative phosphorylation that causes pathology, as MPTP typically only causes a mild
decrease in ATP levels and falls short of levels required to cause significant energy
depletion (Perier and Vila 2012), and deficiency in a component of Complex I does not lead
to selective dopaminergic neural death (Sterky et al. 2012). Therefore, MPTP
neurodegeneration is not necessarily caused by energy depletion. More likely, a shift in
energy balance is a contributing factor to Parkinson’s disease (Krug et al. 2014), as we
recently showed identifying the pathways of defense of dopaminergic neurons in response to
MPP+ before cytotoxicity manifests.
Arch Toxicol. Author manuscript; available in PMC 2016 May 01.
Maertens et al.
Page 3
Author Manuscript
Another consequence of the ETC disruption is increased ROS generated by impaired
mitochondria. This may in turn cause oxidative damage to Complex I, initiating a spiral of
decreased mitochondrial efficiency and increased ROS. ROS can cause peroxidation of the
lipids, disrupting the normal binding of cytochrome c to the mitochondrial membrane, and
facilitates the pro-apoptotic release of cytochrome c to the cytosol (Perier and Vila 2012).
Mitochondria-derived ROS has also been shown to damage lysosomal membranes in MPTPintoxicated mice, leading to an impairment of lysosomal function and defective autophagic
activity (Dehay et al. 2010), including mitochondrial autophagy (Ivatt and Whitworth 2014).
In addition to proteins and lipids, MPTP-intoxicated mice also exhibit oxidative damage to
nuclear and mitochondrial DNA (Hoang et al. 2009). Despite the centrality of the
intracellular, mitochondrial-generated ROS from neurons, there may be other contributors to
ROS in the context of PD/MPTP toxicity—for example, astrocytes or microglia.
Author Manuscript
Another key component of MPTP toxicity is microtubule disruption. MPP+ is believed to
lead to hyperphosphorylation of microtubule-associated protein tau (MAPT), which leads to
microtubule instability (Cappelletti et al. 2001). Depolymerization of MTs is one suggested
reason for the selective vulnerability of dopaminergic (DA) neurons by toxins such as
MPTP, paraquat and rotenone, as dopaminergic neurons require axonal transport of neurotransmitters to the striatum for dopamine release (Ren et al. 2005). The traffic along the
axonal length of DA neurons requires intricate coordination between MTs and the motor
proteins to ensure that dopamine is transported successfully through vesicle transport.
Depolymerization—or, less acutely, an impairment of coordinated traffic—can lead to an
impairment of neural function. Furthermore, in neurons, mitochondria are actively
transported throughout the cell body; the combination of impaired mitochondrial activity
and impaired transport is likely key to the toxic outcome (Sterky et al. 2012).
Author Manuscript
The final step of MPTP toxicity, apoptosis, is likely the result of several pathways that
combine to produce cell death. Apoptosis is thought to be generated through a
mitochondrial-initiated, BAX-dependent process. Complex I inhibition does not directly
trigger mitochondrial cytochrome c release but instead increases the “releasable” pool of
cytochrome c in the mitochondrial membrane— increasing the magnitude of the signal that
can be released when activated by BAX (Perier and Vila 2012).
In summary, while MPTP toxicity has an agreed-upon origin (mitochondrial disruption),
there is still much to be learned about the exact Pathway of Toxicity, and the toxicity
mechanism likely involves alterations of several pathways along key points (Krug et al.
2014). Here, we demonstrate for the first time, how a Pathway of Toxicity can be deduced
with bioinformatics approaches from a rather limited omics dataset.
Author Manuscript
Materials and methods
Data
Dataset GDS2053, which represented a small study of 12 samples based on the Affymetrix
Murine Genome U74A Array from MPTP-treated mice (Miller et al. 2004), was
downloaded from GEO with GEOQuery (Davis and Meltzer 2007) and checked for outliers
Arch Toxicol. Author manuscript; available in PMC 2016 May 01.
Maertens et al.
Page 4
Author Manuscript
via the IAC function in WGCNA (Langfelder and Horvath 2008). The top 5000 genes were
filtered using the rank means function in WGCNA.
Author Manuscript
WGCNA uses correlation to determine the strength of the network connection typically, β
can be chosen to fit the network to a scale-free topology A = [aij] = [|cor(xi, xj)|β. Here, β
was chosen as 7 based on the lowest value that produced a scale-free topology in the
network. A Topological Overlap Metric (TOM) was calculated as described in (Yip and
Horvath 2007), and probes were clustered and assigned to modules using the
“blockwisemodule” function with a signed Spearman rank correlation with β = 7, and a deep
split level of 2 (which represents a medium level of sensitivity in terms of how modules are
detected), a minimum module size of 40, and clustering based on the Dynamic Tree Cut
algorithm (Langfelder et al. 2008). Eigengenes were calculated from each module, and p
values were calculated based on the functions in the WGCNA package (Langfelder and
Horvath 2007). The network was based on the TOM calculated from an unsigned network
with the same settings used for module detection, filtered down to only genes in the
statistically significant modules and with an edge weight >0.20.
Enrichment analysis
Probes for each module were entered into DAVID (Dennis et al. 2003) and analyzed for
enrichment with a stringency set to high and all other settings at default.
Visualization
All networks were visualized in Cytoscape 3.0 (Shannon et al. 2003) with the yFiles circular
layout or spring-embedded bio-layout.
Author Manuscript
Genetic regulatory network
All probes mapped to each module were entered into Molecular Signatures Database
(MSigDB) (Subramanian et al. 2005), and all the top 100 motifs and microRNA binding
sites with a false discovery corrected p value <0.01 were retrieved. Statistically significant
curated gene sets were retrieved as well.
Text-mining was performed in PubMed with the name of the transcription factors and
Parkinson’s disease as a MeSH term and “1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine”
OR “1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine” OR “mptp.” In the case of fewer than 5
abstracts returned results were manually inspected for relevance.
Author Manuscript
Gene symbols were entered into FANTOM4 EdgeEx-pressDB (Severin et al. 2009); cases of
ambiguity were resolved manually. ChIP interactions, siRNA experiments or published
interactions were considered as experimental evidence. Predicted evidence was either the
transcription factor binding predictions or mirRNA predictions.
Arch Toxicol. Author manuscript; available in PMC 2016 May 01.
Maertens et al.
Page 5
Author Manuscript
Results
WGCNA clustered probes by function and captured the relevant pathways
Author Manuscript
Author Manuscript
While correlation networks—often referred to as “guilt-by-association” analysis
(Quackenbush 2003)—are commonly used to derive networks de novo from microarray
data, weighted gene correlation network analysis (WGCNA) offers several advantages.
Unweighted correlation networks typically establish a hard cut-off for a link, but WGCNA
links each gene by a weight, and this network is used to derive a Topological Overlap
Metric, which is most simply thought of as a measurement of gene interconnectivity. This
combines the advantages of a correlation network with the insights gleaned from a graphtheoretical approach; it is typically more sensitive to “weaker” connections among genes
that may be significant, while at the same time it is somewhat more robust to noise
(Langfelder and Hovarth 2008). We chose MPTP toxicity, a commonly used toxicity model
for Parkinson’s disease, and located a publicly available GEO dataset produced from tissue
isolated from the substania nigra of male C57BL/6J mice dosed at 10 weeks of age with a
total of three doses of 30 mg/kg MPTP dosed via i.p. or saline control and killed either 24 h
or 7 days after the final dose of neurotoxin. Biological replicates were pooled, and twelve
arrays in total were used with four arrays per group. The initial dataset was downloaded as
normalized data from the GEO dataset file, filtered for the top 5000 probes by rank mean
expression and used to produce the initial network, which was divided into modules based
on the Topological Overlap Metric as clustered by the Dynamic Tree Cut algorithm (See
Supplemental Figure 1). The modules were summarized as “eigengenes”— essentially, the
first principal component of all genes’ expression for that module, which represents an
“expression signature.” The eigengenes are then correlated with the phenotypic label, in this
case time (Control, Day 1 and Day 7). Five modules were statistically significant, with the
Midnight Blue module having the highest correlation (Table 1). Unassigned genes had no
significant correlation, as would be expected. Therefore, WGCNA identified in an
untargeted approach total of 1247 genes in five clusters that were significantly correlated
with the phenotype label.
Author Manuscript
One of the underlying premises of WGCNA is that genes with a similar function will cluster
together. In order to ensure both that the clusters produced were biologically meaningful and
that they captured the known biological processes involved in MPTP toxicity, we analyzed
the modules using DAVID for over-represented annotations. All significant modules except
one, the Midnight Blue module, were significantly enriched for terms when investigated by
DAVID, and the DAVID enrichment clusters captured the known biology of MPTP toxicity
(e.g., apoptosis—Magenta module; oxidative phosphorylation/Parkinson’s disease— Brown
module) (Supplemental Table 1). The resulting network was visualized in Cytoscape (Fig.
1). One advantage of WGCNA is that it is a dimensionality reduction technique allowing for
insight into the interrelationship among the modules. As can be seen from the network (Fig.
1), three modules (Brown, Salmon and Magenta) were fairly tightly interconnected, while
the Midnight Blue module appeared as a sparse module, which connected the Cyan module
with the other three. This suggests that the Midnight Blue module may act to coordinate the
distinct functions of the other three modules, which may be mediated by transcription factor
TCF3 (see discussion below).
Arch Toxicol. Author manuscript; available in PMC 2016 May 01.
Maertens et al.
Page 6
Author Manuscript
Correlation networks typically have a high rate of false-positives and provide no insight into
regulatory mechanisms. Therefore, to bring this approach closer to a mechanistically
specified network and to better characterize the underlying biology, each module was
analyzed for gene signatures in the Chemical and Genetic Perturbation subset of MSigDB as
well as for over-represented transcription factor binding sites. Each module, except for the
Magenta module, was substantially enriched for genes involved in Alzheimer’s disease
(Table S2), and while Alzheimer’s disease has a different mechanism of neuronal
degeneration compared to Parkinson’s, this does indicate that the approach selected genes
that are involved in neurodegenerative disease. Furthermore, it indicates that while the
Midnight Blue module had no annotations to establish the functional significance of the
cluster, the genes identified are related to neurodegeneration.
Modules were enriched for transcription factors relevant to Parkinson’s disease
Author Manuscript
Author Manuscript
One biological reason for correlation of gene expression is common transcription factors or
microRNAs. Therefore, each module was also analyzed in MSigDB for enriched
transcription factor binding sites with an FDR corrected p value of <0.01. This generated a
list of 114 candidate transcription factor binding motifs that were enriched in the modules
(of which 25 had no known transcription factor) and 23 microRNA binding sites. All
modules had more than 10 predicted enriched motifs, and there was substantial overlap
between enriched motifs among the modules. Candidate transcription factors and microRNA
were text-mined for association with either Parkinson’s or MPTP toxicity, and any
transcription factor with more than two articles for Parkinson’s and/or MPTP toxicity was
considered relevant for building a genetic regulatory network (Table 2). This methodology
found transcription factors that were well known for Parkinson’s—JUN and NRF2, as well
as ELK1, which both had literature evidence for Parkinson’s and was a transcription factor
in the Parkinson’s Pathway in the PANTHER Database (Mi et al. 2013). Additionally, one
of the transcription factor binding sites—SP1—had relatively few articles for Parkinson’s
disease, but did have binding motifs enriched in each of the modules (Table 2). SP1 was the
only transcription factor with annotations for Parkinson’s that was identified by MSigDB as
relevant to the module with the strongest correlation with time, Midnight Blue module. The
Cyan module had many transcription factors that were not shared with other modules, while
the Brown module, in keeping with its size, had the largest number of potential transcription
factors.
Transcription factors significantly improved the number of genes that could be connected
in a component
Author Manuscript
For each module, all of the genes that could be located in the FANTOM4 database were
analyzed with and without the subset of transcription factors both significant for that module
and identified as being relevant to Parkinson’s disease to form the basis of a genetic
regulatory network for that module. All modules except one—Midnight Blue— contained a
subset of genes that were connected by experimentally verified regulatory interactions in
FANTOM4 (ChIP data, siRNA or published interactions), indicating that the modules
consisted of genes that could be connected to each other with experimental data (Table 3).
Arch Toxicol. Author manuscript; available in PMC 2016 May 01.
Maertens et al.
Page 7
Author Manuscript
However, the “connected component”—that is to say, the largest subset of genes and
proteins that were interconnected with each other—grew substantially with the addition of
the predicted transcription factors as identified by MSigDB and text-mining, even when
restricted to experimental evidence; the percent of the modules connected by experimentally
verified interactions ranged from a low of 70–80 % for each module and was 100 % for the
Midnight Blue module (Table 3). For each module, the transcription factor that had, by far,
the highest number of interactions was SP1, and it also had substantial experimental
evidence of interactions (see Figs. 2, 4, 5, S2, S3). Within the Brown module, a subnetwork
centered on SP1 and JUN indicated that it not only activated JUN but was connected to
several downstream components as well (see Fig. 3). The Cyan module showed SP1 as a
main hub and SREBF1 as a smaller hub when restricted to experimental evidence (see Fig.
4). Within the Midnight Blue module, even when restricted to evidence of 4 ChIP
experiments, SP1 remained a significant hub (see Fig. 5).
Author Manuscript
SP1 is a ubiquitously expressed transcription factor that regulates a sweeping number of
genes during development and other cellular functions. SP1 is known to play a key role in
tissue differentiation; knockout mice are embryo-lethal and have multiple abnormalities
(OMIM). SP1 is also known to play a role in cell-cycle inhibition (Deniaud et al. 2009), and
over-expression leads to apoptosis (Chuang et al. 2009). Furthermore, SP1 is known to
regulate the dopamine transporter (Wang and Bannon 2005) and is involved in several
neurodegenerative diseases (Qiu et al. 2006) (Santpere et al. 2006). SP1 appears to be
acetylated in neurons in response to oxidative stress and works in tandem with histone
deacetylases to prevent cell death (Ryu et al. 2003); acetylation is but one of many posttranslational modifications that expand SP1’s response repertoire.
Author Manuscript
SP1 was not present in the modules, nor was it among the genes differentially expressed,
even with the most generous of cut-off values for significance. However, SP1 protein and
mRNA levels have been shown to increase following MPP+ dosing in PC12 cells by
approximately 1.5-fold, which was blocked by antioxidant treatment (Ye et al. 2013). The
lack of appearance of SP1 among the genes differentially expressed or in the modules may
simply reflect that SP1 mRNA rises only modestly (or perhaps briefly); alternatively, it is
regulated by means other than an increase in mRNA levels and the signal increase is
therefore nonlinear compared to mRNA levels (Courey et al. 1989). As SP1 is constitutively
expressed rather than inducible, it may also act as a preliminary sensor that initiates the
cascade.
Author Manuscript
Genes from all modules were combined into a genetic regulatory network based on
FANTOM4 interactions as follows: (1) evidence limited to published interactions and
siRNA perturbation data, (2) published/perturbation data with the addition of ChIP data and
(3) all evidence, including predicted transcription factor binding sites. Even when restricted
to published evidence, the resulting genetic regulatory network consisted of a connected
component of 256 genes with several hubs (Fig. 6). Including ChIP data extended it to 782
and predicted transcription factor bind sites to 830. In addition to SP1, the network hubs
consist of some candidates well known for their role in Parkinson’s (STAT3, JUN) but also
produced other candidates that have been implicated in Parkinson’s: SREBF1 has previously
been identified as a risk locus for sporadic Parkinson’s disease (Do et al. 2011), and in a
Arch Toxicol. Author manuscript; available in PMC 2016 May 01.
Maertens et al.
Page 8
Author Manuscript
recent RNAi screening study, it was implicated in the control of the PTEN-induced kinase 1
(PINK1)/Parkin pathways that control the autophagic destruction of mitochondria (Ivatt and
Whitworth 2014).
One hub identified in the reconstructed GRN from FANTOM4, HDAC1, has been
implicated in cell survival in neurotoxicity to dopaminergic neurons in vitro and ischemia in
vivo (Kim et al. 2008); HDAC1 was also a hub in the WGCNA network, and all the genes
connected to HDAC1 in the FANTOM4 GRN by two degrees were connected in WGCNA.
The WGCNA network also suggested a protein, LANCL1, that was connected to both
HDAC1 and STAT3. LANCL1 binds glutathione and is believed to play a role in neuronal
survival following oxidative insult (Zhong et al. 2012), and its connection to HDAC1 and
STAT3 seems plausible.
Author Manuscript
Author Manuscript
Author Manuscript
One of the smaller hubs, ZNF148 (a zinc-binding transcription factor), was a hub within
both the FANTOM4 and the Midnight Blue module. Of the 18 genes directly connected to
ZNF148 in the original WGCNA network (which included MAPT), four were also linked by
predicted interactions in FANTOM4. ZNF148 (also referred to as ZPB89) is not present on
any pathway in Panther or KEGG and has a relatively sparse literature base with no
indication of any role in Parkinson’s. Of the four genes (CBX3, DDX6, SYNGR1 and
C20orf27) predicted to interact with ZNF148 in FANTOM4 and also in the WGCNA
network, only SYNGR1 has evidence of involvement with neuroplasticity (Janz et al. 1999).
ZNF148, however, is known to play a role in apoptosis (Zhang et al. 2010), is verified by
proteomic studies as expressed in the substantia nigra (Chen et al. 2012) and would be an
interesting candidate for further study. ATF4, which has recently been identified by us and
others in independent high-throughput studies as a key transcriptional factor in MPTP
toxicity (Ye et al. 2013, Krug et al. 2014), was also present as a small hub containing mostly
protein–protein interaction connections in the network when restricted to experimentally
verified interactions. Similarly, TCF3 had relatively few experimentally verified reactions
and is thus relatively small in the graph; however, an expanded subnetwork that included
predicted transcription factor binding sites, even when restricted to a high stringency level,
would have been substantially larger. TCF3 was in the Midnight Blue module, and the Cyan,
Salmon and Brown modules were all enriched for TCF3 binding motifs. This is likely a case
where the relative importance of a gene is underestimated based on the lack of available
experimental data in comparison with the better-studied SP1. One pitfall of this
methodology is that it will be biased toward comparatively well-characterized, GC-rich
transcription factor binding sites. In theory, examining all highly co-ordinated genes in the
WGCNA network would not have such a bias, but given the high rate of false-positives
there would have to be another method of confirming the causative agent of co-ordinated coexpression.
One of the transcription factor binding sites that were consistently ranked by MSigDB
across all modules (PAX4) had no textual evidence for involvement with Parkinson’s,
although it does appear to be expressed in the brain. However, there was no experimental
evidence in FANTOM4 that it bound to any of the targets in the modules, the predicted
targets were quite sparse, and its inclusion would not have fundamentally changed the
architecture of the network. Similarly, binding sites for LEF1 were also statistically overArch Toxicol. Author manuscript; available in PMC 2016 May 01.
Maertens et al.
Page 9
Author Manuscript
represented in all the modules, and LEF1 was present in one of the modules. While it was
therefore a “hub” in the FANTOM4 GRN if both predicted and published interactions were
included, it was not a hub in the WGCNA network, likely indicating that it is of minimal
importance to the underlying physiological network. This indicates that the dimensionality
reduction achieved by clustering the genes into modules helps eliminate spurious
transcription factor binding sites and that using text-mining (or restricting to published
interactions) helps to narrow down the candidate transcription factors and improves the
results. Furthermore, the resultant genetic regulatory network does not merely reflect
nonspecific predicted transcription factor binding motifs that are enriched in any subset of
genes, but is likely enriched for biologically relevant targets.
Discussion
Author Manuscript
Author Manuscript
Current analysis of microarray data in toxicology typically does not take advantage of the
data-mining and bioinformatics tools available to interpret the underlying mechanisms but
remains at the level of “biomarker” or signature identification, either generating a relatively
small list of genes differentially expressed using inferential statistics, or over-representation
analysis, which is highly dependent on pathway annotations. We chose an existing dataset,
originally used to identify a few genes as signatures of MPTP toxicity in vivo, in order to
explore an alternative method that would offer more insight into dynamics of gene
expression compared to inferential statistics and would not be dependent on pathway
annotations. WGCNA offers many advantages for analyzing microarray data: It is
unsupervised, and, unlike correlation networks that are based solely on a Pearson or
Spearman correlation, it preserves weak links—capturing interactions that may be small, but
nonetheless be biologically interesting. This may be especially relevant to toxicology, as the
effects may be subtle and distributed among many pathways. The dimensionality reduction
achieved has the advantage of preserving the connection between the clusters; the HDAC1
subnetwork in Fig. 7 includes almost equal parts of genes from the Magenta and Brown
modules.
Author Manuscript
As this represented a fairly unsophisticated approach to text-mining transcription factor
candidates, it is quite probable that the proposed regulatory network is only a “10,000-foot”
view; many of the transcription factors may have had textual evidence of being involved in
physiological processes that are relevant to MPTP toxicity—e.g., oxidative stress or
apoptosis. Although extending the text-mining in such a way would likely have increased
the false-positives, it could also have fine-tuned the map in some of the “neighborhoods.”
Objective literature mining by search engines is a key for an unbiased use of the existing
literature, clearly preferable over the cherry-picking of studies in interpreting individual
results. MPTP’s Pathway of Toxicity is aided markedly by the fact that it serves as an
animal model for a relatively well-researched disease such as Parkinson’s; depending on
MPTP/MPP+ literature would have produced a much smaller subset of candidate
transcription factors and no microRNAs, perhaps reflecting the relatively immature literature
base from microRNAs compared to transcription factors.
Furthermore, just as the “connected component” likely contains some regulatory
connections that are artifacts, the unconnected component contains both genes that are
Arch Toxicol. Author manuscript; available in PMC 2016 May 01.
Maertens et al.
Page 10
Author Manuscript
spurious correlations and genes that are unconnected due to lack of data about the probable
regulatory mechanism. Disappointingly, the microRNAs that were identified did not have
any regulatory connections; this may reflect the fact that microRNAs simply have an
inadequate dataset, and it is likely that multiple microRNAs are involved but are simply
invisible in this analysis. Surprisingly, one of the “unconnected” genes in the Brown module
was MAO-A (monoamine oxidase A). Although MPTP is metabolized much more
efficiently by MAO-B, MAO-A is possibly involved in dopaminergic cell death in neurons
(Naoi et al. 2012) and there is evidence that SP1 binds to the promoter of MAO-A (Zhu et
al. 1994).
Author Manuscript
Interestingly, within the Midnight Blue module, two proteins that had relatively weak
evidence of connection to SP1, AQP4 (Aquaporin-4) and TUB (Tubby protein) were not in
the final FANTOM4 genetic regulatory network, but were examined for evidence of
involvement in MPTP toxicity-related processes, as both have knockout mice models.
Aquaporin-4 knockout mice are more prone to MPTP toxicity (Fan et al. 2008), and
although Aquaporin-4 may or may not be regulated by SP1, it likely plays a role in the
ultimate phenotypic consequences of MPTP toxicity (and perhaps Parkinson’s as well).
Tubby protein knockout mice have a primary phenotype of obesity but also display
neurodegeneration. There is some evidence that TUB is a regulator of microglial
phagocytosis through the MerTK receptor (Caberoy et al. 2012). The exact nature and role
of TUB in MPTP toxicity, however, remain speculative. Nonetheless, neither the genes
unconnected to the larger network nor the weaker links in the network that lack substantial
experimental evidence should be discarded wholesale.
Author Manuscript
Mitochondrial disruption is a commonality for a variety of neurotoxins and
neurodegenerative diseases, but the exact route between mitochondrial disruption and the
phenotype is unclear. MPTP, like other toxins, may work primarily to disrupt the
mitochondria, but the disruption likely has pleiotropic effects that differ from other toxins
and disease states. Depending on annotations to reveal physiological function (or,
alternatively, discarding a cluster because of lack of annotations) may overlook useful
information about toxic processes. In this case, the Midnight Blue module contained genes
known or strongly suspected to be involved in Parkinson’s or MPTP toxicity (MAPT,
SYNGR1), as well as genes known to be involved in neuropathology (THOP1, which
cleaves amyloid precursor protein) (Pollio et al. 2008). It also suggested novel candidates
that are plausibly involved in the degenerative process (AQP4 and TUB), neither of which
were on existing Parkinson’s pathways (Panther, KEGG) and both of which had an
inadequate literature depth on which to base enrichment analysis.
Author Manuscript
The pronounced promiscuousness of SP1 binding sites entails that many, if not most, of the
predicted interactions are spurious and the experimentally verified interactions may be
irrelevant within the particular context of MPTP toxicity in dopaminergic neurons.
However, given the statistically significant over-representation of SP1 motifs in all the
modules, the centrality of SP1 to the predicted network, the literature evidence of
involvement in Parkinson’s, dopamine regulation and MPTP toxicity, and the experimental
evidence of interactions with known signaling networks (such as JUN) involved in
Parkinson’s and MPTP, SP1 is likely necessary (though not sufficient) for MPTP toxicity
Arch Toxicol. Author manuscript; available in PMC 2016 May 01.
Maertens et al.
Page 11
Author Manuscript
and acts to integrate multiple signaling pathways in a combinatorial and complex manner.
The proposed genetic regulatory network offers an advantage compared to a correlation
network insofar as it offers a direction of action, an estimate of transcription factor binding
site strength, multiple lines of evidence and an estimate of the dynamics of gene expression.
Therefore, it can act as scaffolding which further experiments, both in silico and in vitro, can
refine.
Author Manuscript
This study shows that a relatively small gene array study allows for pinpointing mechanistic
information by a combination of correlation approaches with both text-mining and largescale interaction datasets such as FANTOM. Each independently has strengths and
weaknesses and is error-prone. Combining the approaches to narrow done potential
candidates, however, is promising. Nonetheless, any data-mining approach—especially
those that tend to generate false-positives—must go hand-in-hand with confirmation of the
(patho-)physiological sense of the distilled information. These emerging approaches for
Pathway of Toxicity identification can become even more powerful when several
orthogonalomics technologies are employed and different experimental models are
combined.
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
Acknowledgments
Author Manuscript
Funding for “Mapping the Human Toxome by Systems Toxicology” was provided by an NIH Transformation
Research Grant (RO1 ES 020750) and Food and Drug Administration Grant “DNTox-21c Identification of
Pathways of Developmental Neurotoxicity for High Throughput Testing by Metabolomics” (U01 FD 004230) to
Johns Hopkins Bloomberg School of Public Health (PI Thomas Hartung). Alexandra Maertens was supported by an
NIEHS diversity grant.
References
Author Manuscript
Bouhifd M, Hogberg HT, Kleensang A, Maertens A, Zhao L, Hartung T. Mapping the human toxome
by systems toxicology. Basic Clin Pharmacol Toxicol. 2014; 115:1–8.
Caberoy NB, Alvarado G, Li W. Tubby regulates microglial phagocytosis through MerTK. J
Neuroimmunol. 2012; 252:40–48. [PubMed: 22884297]
Cappelletti G, Pedrotti B, Maggioni MG, Maci R. Microtubule assembly is directly affected by
MPP(+)in vitro. Cell Biol Int. 2001; 25:981–984. [PubMed: 11589612]
Chen S, Lu FF, Seeman P, Liu F. Quantitative proteomic analysis of human substantia nigra in
Alzheimer’s Disease, Huntington’s disease and Multiple sclerosis. Neurochem Res. 2012; 37:2805–
2813. [PubMed: 22926577]
Chuang JY, Wu CH, Lai MD, Chang WC, Hung JJ. Overexpression of Sp1 leads to p53 dependent
apoptosis in cancer cells. Int J Cancer. 2009; 125:2066–2076. [PubMed: 19588484]
Courey AJ, Holtzman DA, Jackson SP, Tjian R. Synergistic activation by the glutamine-rich domains
of human transcription factor Sp1. Cell. 1989; 59:827–836. [PubMed: 2512012]
Davis S, Meltzer PS. GEOquery: a bridge between the gene expression omnibus (GEO) and
bioconductor. Bioinformatics. 2007; 23:1846–1847. [PubMed: 17496320]
Dehay B, Bove J, Rodriguez-Muela N, Perier C, Recasens A, Boya P, Vila M. Pathogenic lysosomal
depletion in Parkinson’s disease. J Neurosci. 2010; 30:12535–12544. [PubMed: 20844148]
Deniaud E, Baguet J, Chalard R, Blanquier B, Brinza L, Meunier J, Michallet M-C, Laugraud A, AhSoon C, Wierinckx A, Cas-tellazzi M, Lachuer J, Gautier C, Marvel J, Leverrier Y. Overexpression
Arch Toxicol. Author manuscript; available in PMC 2016 May 01.
Maertens et al.
Page 12
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript
of transcription factor Sp1 leads to gene expression perturbations and cell cycle inhibition. PLoS
One. 2009; 4:e7035. [PubMed: 19753117]
Dennis G Jr. Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA. DAVID: database
for annotation, visualization, and integrated discovery. Genome Biol. 2003; 4:P3. http://
genomebiology.com/2003/4/9/R60. [PubMed: 12734009]
Do CB, Tung JY, Dorfman E, Kiefer AK, Drabant EM, Francke U, Mountain JL, Goldman SM,
Tanner CM, Langston JW, Wojcicki A, Eriksson N. Web-based genome-wide association study
identifies two novel loci and a substantial genetic component for Parkinson’s disease. PLoS Genet.
2011; 7:e1002141. [PubMed: 21738487]
Fan Y, Kong H, Shi X, Sun X, Ding J, Wu J, Hu G. Hypersensitivity of aquaporin 4-deficient mice to
1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyrindine and astrocytic modulation. Neurobiol Aging.
2008; 29:1226–1236. [PubMed: 17353068]
Hartung T. Toxicology for the twenty-first century. Nature. 2009; 460:208–212. [PubMed: 19587762]
Hartung T, McBride M. Food for thought… on mapping the human toxome. Altex. 2011; 28:83–93.
[PubMed: 21625825]
Hoang T, Choi D-K, Nagai M, Wu D-C, Nagata T, Prou D, Wilson GL, Vila M, Jackson-Lewis V,
Dawson VL, Dawson TM, Chessel M-F, Przedborski S. Neuronal NOS and cyclooxygenase-2
contribute to DNA damage in a mouse model of Parkinson disease. Free Radical Biol Med. 2009;
47:1049–1056. [PubMed: 19616617]
Ivatt R, Whitworth AJ. SREBF1 links lipogenesis to mitophagy and sporadic Parkinson’s disease.
Autophagy. 2014; 10:33–34.
Janz R, Sudhof TC, Hammer RE, Unni V, Siegelbaum SA, Bolshakov VY. Essential roles in synaptic
plasticity for synaptogyrin I and synaptophysin I. Neuron. 1999; 24:687–700. [PubMed:
10595519]
Kim D, Frank CL, Dobbin MM, Tsunemoto RK, Wu D, Peng PL, Guan J-S, Lee B-H, Moy LY, Giusti
P, Broodie N, Mazitschek R, Delalle I, Haggarty SJ, Neve RL, Lu YM, Tsai L-H. Deregulation of
HDAC1 by p25/Cdk5 in neurotoxicity. Neuron. 2008; 60:803–817. [PubMed: 19081376]
Kleensang A, Maertens A, Rosenberg M, Fitzpatrick S, Lamb J, Auerbach S, Brennan R, Crofton KM,
Gordon B, Fornace AJ Jr. Gaido K, Gerhold D, Haw R, Henney A, Ma’ayan A, McBride M,
Monti S, Ochs MF, Pandey A, Sharan R, Stierum R, Tugendreich S, Willett C, Wittwehr C, Xia J,
Patton GW, Arvidson K, Bouhifd M, Hogberg HT, Luechtefeld T, Smirnova L, Zhao L, Adeleye
Y, Kanehisa M, Carmichael P, Andersen EM, Hartung T. Pathways of Toxicity. Altex. 2014;
31:53–61. [PubMed: 24127042]
Krug AK, Gutbier S, Zhao L, Pöltl D, Kullmann C, Ivanova V, Förster S, Jagtap S, Meiser J, Leparc
G, Schildknecht S, Adam M, Hiller K, Farhan H, Brunner T, Hartung T, Sachinidis A, Leist M.
Transcriptional and metabolic adaptation of human neurons to the mitochondrial toxicant MPP(+).
Cell Death Dis. 2014; 5:e1222. [PubMed: 24810058]
Langfelder P, Horvath S. Eigengene networks for studying the relationships between co-expression
modules. BMC Syst Biol. 2007; 1:54. [PubMed: 18031580]
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC
Bioinforma. 2008; 9:559.
Langfelder P, Zhang B, Horvath S. Defining clusters from a hierarchical cluster tree: the dynamic tree
cut package for R. Bioinformat. 2008; 24:719–720.
Mi H, Muruganujan A, Casagrande JT, Thomas PD. Large-scale gene function analysis with the
PANTHER classification system. Nat Protoc. 2013; 8:1551–1566. [PubMed: 23868073]
Miller RM, et al. Dysregulation of gene expression in the 1-methyl-4-phenyl-1, 2, 3, 6tetrahydropyridine-lesioned mouse substantia nigra. J neurosci. 2004; 24:7445–7454. [PubMed:
15329391]
Naoi M, Maruyama W, Inaba-Hasegawa K. Type A and B monoamine oxidase in age-related
neurodegenerative disorders: their distinct roles in neuronal death and survival. Curr Top Med
Chem. 2012; 12:2177–2188. [PubMed: 23231395]
Perier C, Vila M. Mitochondrial biology and Parkinson’s disease. Cold Spring Harb Perspect Med.
2012; 2:a009332. [PubMed: 22355801]
Arch Toxicol. Author manuscript; available in PMC 2016 May 01.
Maertens et al.
Page 13
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript
Pollio G, Hoozemans JJ, Andersen CA, Roncarati R, Rosi MC, van Haastert ES, Seredenina T,
Diamanti D, Gotta S, Fiorentini A, Magnoni L, Raggiaschi R, Rozemuller AJ, Casamenti F,
Carica-sole A, Terstappen GC. Increased expression of the oligo-peptidase THOP1 is a
neuroprotective response to Abeta toxicity. Neurobiol Dis. 2008; 31:145–158. [PubMed:
18571100]
Qiu Z, Norflus F, Singh B, Swindell MK, Buzescu R, Bejarano M, Chopra R, Zucker B, Benn CL,
DiRocco DP, Cha JH, Ferrante RJ, Hersch SM. Sp1 is up-regulated in cellular and transgenic
models of Huntington disease, and its reduction is neuroprotective. J Biol Chem. 2006;
281:16672–16680. [PubMed: 16595660]
Quackenbush J. Genomics. Microarrays-guilt by association. Science. 2003; 302:240–241. [PubMed:
14551426]
Ren Y, Liu W, Jiang H, Jiang Q, Feng J. Selective vulnerability of dopaminergic neurons to
microtubule depolymerization. J Biol Chem. 2005; 280:34105–34112. [PubMed: 16091364]
Ryu H, Lee J, Olofsson BA, Mwidau A, Dedeoglu A, Escudero M, Flemington E, Azizkhan-Clifford J,
Ferrante RJ, Ratan RR. Histone deacetylase inhibitors prevent oxidative neuronal death
independent of expanded polyglutamine repeats via an Sp1-dependent pathway. Proc Natl Acad
Sci USA. 2003; 100:4281–4286. [PubMed: 12640146]
Santpere G, Nieto M, Puig B, Ferrer I. Abnormal Sp1 transcription factor expression in Alzheimer
disease and tauopathies. Neurosci Lett. 2006; 397:30–34. [PubMed: 16378688]
Schober A. Classic toxin-induced animal models of Parkinson’s disease: 6-OHDA and MPTP. Cell
Tissue Res. 2004; 318:215–224. [PubMed: 15503155]
Severin J, Waterhouse AM, Kawaji H, Lassmann T, van Nimwegen E, Balwierz PJ, de Hoon MJ,
Hume DA, Carninci P, Hayashizaki Y, Suzuki H, Daub CO, Forrest AR. FANTOM4 EdgeExpressDB: an integrated database of promoters, genes, microR-NAs, expression dynamics and
regulatory interactions. Genome Biol. 2009; 10:R39. [PubMed: 19374773]
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T.
Cytoscape: a software environment for integrated models of biomolecular interaction networks.
Genome Res. 2003; 13:2498–2504. [PubMed: 14597658]
Snyder SH, D’Amato RJ. MPTP: a neurotoxin relevant to the pathophysiology of Parkinson’s disease
—The 1985 George C. Cotzias Lecture. Neurology. 1986; 36:250. [PubMed: 3080696]
Sterky FH, Hoffman AF, Milenkovic D, Bao B, Paganelli A, Edgar D, Wibom R, Lupica CR, Olson L,
Larsson NG. Altered dopamine metabolism and increased vulnerability to MPTP in mice with
partial deficiency of mitochondrial complex I in dopamine neurons. Hum Mol Gen. 2012;
21:1078–1089. [PubMed: 22090423]
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy
SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based
approach for interpreting genome-wide expression pro-files. Proc Natl Acad Sci USA. 2005;
102:15545–15550. [PubMed: 16199517]
Wang J, Bannon MJ. Sp1 and Sp3 activate transcription of the human dopamine transporter gene. J
Neurochem. 2005; 93:474–482. [PubMed: 15816870]
Ye Q, Zhang X, Huang B, Zhu Y, Chen X. Astaxanthin suppresses MPP-induced oxidative damage in
PC12 cells through a Sp1/NR1 signaling pathway. Mar Drugs. 2013; 11:1019–1034. [PubMed:
23538867]
Yip AM, Horvath S. Gene network interconnectedness and the generalized topological overlap
measure. BMC Bioinforma. 2007; 8:22.
Zhang CZ, Chen GG, Lai P. Transcription factor ZBP-89 in cancer growth and apoptosis. Biochim
Biophys Acta Rev Cancer. 2010; 1806:36–41.
Zhong WX, Wang YB, Peng L, Ge XZ, Zhang J, Liu SS, Zhang XN, Xu ZH, Chen Z, Luo JH.
Lanthionine synthetase C-like protein 1 interacts with and inhibits cystathionine beta-synthase: a
target for neuronal antioxidant defense. J Biol Chem. 2012; 287:34189–34201. [PubMed:
22891245]
Zhu QS, Chen K, Shih JC. Bidirectional promoter of human monoamine oxidase A (MAO A)
controlled by transcription factor Sp1. J Neurosci. 1994; 14:7393–7403. [PubMed: 7996184]
Arch Toxicol. Author manuscript; available in PMC 2016 May 01.
Maertens et al.
Page 14
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript
Fig. 1.
Network generated by WGCNA, colored by module, using spring-embedded bio-layout
based on edge strength
Arch Toxicol. Author manuscript; available in PMC 2016 May 01.
Maertens et al.
Page 15
Author Manuscript
Author Manuscript
Author Manuscript
Fig. 2.
Author Manuscript
Brown module, identified transcription factors in grey. Legend the Brown module formed a
dense of network of regulatory interactions centered on SP1. Self-loops indicate a gene
interacts with itself
Arch Toxicol. Author manuscript; available in PMC 2016 May 01.
Maertens et al.
Page 16
Author Manuscript
Author Manuscript
Author Manuscript
Fig. 3.
SP1, JUN and STAT1 subnetwork from the Brown module. Legend green indicates ChIP
data; red indicates perturbation experiment; yellow, published protein–DNA interactions,
and purple indicates protein–protein interaction. Node size is proportional to predicted
dynamics of the gene, and darker nodes indicate higher scaled expression levels. Because
the FANTOM4 database gives an estimate of the dynamics of gene expression, the resulting
gene regulatory network can be used as the foundation for building a dynamic model
Author Manuscript
Arch Toxicol. Author manuscript; available in PMC 2016 May 01.
Maertens et al.
Page 17
Author Manuscript
Author Manuscript
Author Manuscript
Fig. 4.
Cyan module with TFs identified as indicated in grey; SP1 is in the middle. Legend edges
represent experimentally verified interactions; straight lines are ChIP experiments or
protein-protein interactions, wavy lines are siRNA preturbation experiments. All nodes were
connected when predicted interactions were included (data not shown)
Author Manuscript
Arch Toxicol. Author manuscript; available in PMC 2016 May 01.
Maertens et al.
Page 18
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript
Fig. 5.
Midnight blue module; SP1 interactions verified with 4 ChIP experiments
Arch Toxicol. Author manuscript; available in PMC 2016 May 01.
Maertens et al.
Page 19
Author Manuscript
Author Manuscript
Author Manuscript
Fig. 6.
Author Manuscript
Genetic regulatory network based on published interactions. Legend node label is
proportionate to hub status as determined by edge count. Self-interactions were deleted for
visual clarity
Arch Toxicol. Author manuscript; available in PMC 2016 May 01.
Maertens et al.
Page 20
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript
Fig. 7.
HDAC1 subnetwork from FANTOM4; all genes within one or two degrees of HDAC1 in
the FANTOM4 network (not including SP1); single leaves deleted for visual clarity. In the
WGCNA network, all the above were linked to HDAC
Arch Toxicol. Author manuscript; available in PMC 2016 May 01.
Maertens et al.
Page 21
Table 1
Author Manuscript
Modules correlated with time
Module
Correlation
p value
Magenta
0.7996246
1.80E–03
Salmon
0.76605824
3.67E–03
Brown
0.58916781
4.38E–02
Cyan
0.69331419
1.24E–02
Midnight Blue
0.94604195
3.29E–06
Unassigned
0.13829676
6.68E–01
Five of the modules produced were significantly correlated; signifi-cance is calculated via a permutation test
Author Manuscript
Author Manuscript
Author Manuscript
Arch Toxicol. Author manuscript; available in PMC 2016 May 01.
Maertens et al.
Page 22
Table 2
Author Manuscript
Candidate transcription factors associated with Parkinson’s and MPTP via text-mining; p values based on
enrichment in MSigDB C3 gene sets
Transcription factor
Abstract for Parkinson’s
MPTP/MPP±
Module
FDR corrected p value
4451
729
Brown
3.44E–08
NRFR2
59
25
Salmon
6.23E–03
FOXF2
21
1
Brown
1.69E–07
SP1
12
2
JUN
Brown
4.25E–26
Cyan
2.51E–06
Magenta
8.08E–05
Midnight Blue
2.53E–03
Author Manuscript
Salmon
7.85E–05
ATF4
12
2
Brown
9.14E–07
TCF3
11
6
Brown
9.20E–07
Cyan
5.82E–04
Salmon
2.52E–02
Brown
3.14E–15
Magenta
8.08E–05
Brown
3.44E–08
Salmon
2.52E–02
Brown
3.76E–06
ELK1
3
AP1
3
1
STAT1
7
2
NRF1
6
3
Brown
2.34E–06
Cyan
7.59E–04
Author Manuscript
SRY
6
Cyan
4.84E–03
MIR-132
5
Brown
3.76E–06
SREBF1
5
ATF3
4
SRY
MIR221
MEF2A
2
ELK1
Cyan
7.59E–04
Cyan
4.84E–03
6
Cyan
4.84E–03
2
Cyan
4.95E–03
2
1
Cyan
9.52E–03
Magenta
0.000809
Magenta
8.08E–05
Brown
3.14E–15
Author Manuscript
Arch Toxicol. Author manuscript; available in PMC 2016 May 01.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript
Table 3
Connected component—without TFS
Connected component
with TFS
Arch Toxicol. Author manuscript; available in PMC 2016 May 01.
Module
Genes in fantom
Experimental
Predicted
Experimental
Predicted
Salmon
163
14
47
SP1, NRF2, TCF3, AP1
125
132
Midnight Blue
105
0
14
SP1
75
105
Cyan
150
16
41
SP1, NRF1, SRY, SREBF1, MIR221, MEF2A, TCF3
121
130
Brown
463
31
163
SP1, JUN*, FOXF2, ATF4, TCF3, ELK1, STAT1*,
NRF1, MIR132
381
409
Magenta
106
14
26
SP1, ELK1, MEF2A
82
91
Maertens et al.
Addition of predicted transcription factors substantially increased connected component
For each module, gene symbols were entered into FANTOM4 EdgeExpressDB and a predicted regulatory network was drawn based on experimental evidence (ChIP, published interactions and siRNA
experiments), with and without the addition of predicted evidence (predicted transcription factor binding and microRNA). Transcription factors were added based on evidence of significantly overrepresented motifs in MSigDB and textual evidence of involvement in Parkinson’s.
*
In the case of the Brown module, STAT1 and JUN were already in the module. “Connected component” consists of all genes that were not singletons in the predicted regulatory network
Page 23