Group Title: BMC Systems Biology
Title: A Dynamic network of transcription in LPS-treated human subjects
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Title: A Dynamic network of transcription in LPS-treated human subjects
Physical Description: Book
Language: English
Creator: Seok, Junhee
Xiao, Wenzhong
Moldawer, Lyle
Davis, Ronald
Covert, Markus
Publisher: BMC Systems Biology
Publication Date: 2009
 Notes
Abstract: BACKGROUND:Understanding the transcriptional regulatory networks that map out the coordinated dynamic responses of signaling proteins, transcription factors and target genes over time would represent a significant advance in the application of genome wide expression analysis. The primary challenge is monitoring transcription factor activities over time, which is not yet available at the large scale. Instead, there have been several developments to estimate activities computationally. For example, Network Component Analysis (NCA) is an approach that can predict transcription factor activities over time as well as the relative regulatory influence of factors on each target gene.RESULTS:In this study, we analyzed a gene expression data set in blood leukocytes from human subjects administered with lipopolysaccharide (LPS), a prototypical inflammatory challenge, in the context of a reconstructed regulatory network including 10 transcription factors, 99 target genes and 149 regulatory interactions. We found that the computationally estimated activities were well correlated to their coordinated action. Furthermore, we found that clustering the genes in the context of regulatory influences greatly facilitated interpretation of the expression data, as clusters of gene expression corresponded to the activity of specific factors or more interestingly, factor combinations which suggest coordinated regulation of gene expression. The resulting clusters were therefore more biologically meaningful, and also led to identification of additional genes under the same regulation.CONCLUSION:Using NCA, we were able to build a network that accounted for between 8–11% genes in the known transcriptional response to LPS in humans. The dynamic network illustrated changes of transcription factor activities and gene expressions as well as interactions of signaling proteins, transcription factors and target genes.
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BMC Systems Biology B.oMedCentral


Research article [


A dynamic network of transcription in LPS-treated human subjects
Junhee Seok1'2, Wenzhong Xiao1, Lyle L Moldawer4, Ronald W Davis'
and Markus W Covert*3


Address: 'Stanford Genome Technology Center, Palo Alto, California, USA, 2Department of Electrical Engineering, University, Stanford,
California, USA, 3Department of Bioengineering, Stanford University, Stanford, California, USA and 4Department of Surgery, University of
Florida, Gainesville, Florida, USA
E-mail: Junhee Seok jseok@stanford.edu; Wenzhong Xiao wzxiao@stanford.edu; Lyle L Moldawer moldawer@surgery.ufl.edu;
Ronald W Davis dbowe@stanford.edu; Markus W Covert* mcovert@stanford.edu
*Corresponding author


Published: 28 July 2009 Received: 27 October 2008
BMC Systems Biology 2009, 3:78 doi: 10.1186/1752-0509-3-78 Accepted: 28 July 2009
This article is available from: http://www.biomedcentral.com/1752-0509/3/78
2009 Seok 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: Understanding the transcriptional regulatory networks that map out the
coordinated dynamic responses of signaling proteins, transcription factors and target genes over
time would represent a significant advance in the application of genome wide expression analysis.
The primary challenge is monitoring transcription factor activities over time, which is not yet
available at the large scale. Instead, there have been several developments to estimate activities
computationally. For example, Network Component Analysis (NCA) is an approach that can
predict transcription factor activities over time as well as the relative regulatory influence of factors
on each target gene.
Results: In this study, we analyzed a gene expression data set in blood leukocytes from human
subjects administered with lipopolysaccharide (LPS), a prototypical inflammatory challenge, in the
context of a reconstructed regulatory network including 10 transcription factors, 99 target genes
and 149 regulatory interactions. We found that the computationally estimated activities were well
correlated to their coordinated action. Furthermore, we found that clustering the genes in the
context of regulatory influences greatly facilitated interpretation of the expression data, as clusters
of gene expression corresponded to the activity of specific factors or more interestingly, factor
combinations which suggest coordinated regulation of gene expression. The resulting clusters were
therefore more biologically meaningful, and also led to identification of additional genes under the
same regulation.
Conclusion: Using NCA, we were able to build a network that accounted for between 8-1 1%
genes in the known transcriptional response to LPS in humans. The dynamic network illustrated
changes of transcription factor activities and gene expressions as well as interactions of signaling
proteins, transcription factors and target genes.



Background of signaling proteins, transcription factors and target genes
An achievement that would have a major impact on our over time. The primary challenges to such an effort are
understanding of transcriptional regulatory networks development of high-throughput technologies to measure
would be to map out the coordinated dynamic responses transcription factor activities at the genome-scale, and



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computational tools to interpret the data and predict the
structure and dynamics of the underlying networks.

Recent development of high-throughput technologies
has enabled large-scale measurements of biological
signals related to transcription, such as the expression
of target genes and the activities of transcription factors.
For target gene expression, microarrays measure the
expression levels of thousands of genes simultaneously
[1-3]. However, efforts to broadly assess transcription
factor activities on a genome wide scale are much more
limited. Technologies such as chromatin immunopreci-
pitation-on-a-chip can identify all of the DNA binding
sites occupied by a single transcription factor for a given
condition [4,5]. Flow cytometry can also be used to
determine transcription factor activities by labeling
active factors with fluorescently labeled antibodies [6],
but throughput is limited by the number of available
antibodies and colors. As yet, there is no transcription
factor-focused equivalent of the gene expression array,
which would enable monitoring of all transcription
factor activities at a time. Such technology would be
critical to generating a complete dynamic network of
transcription empirically.

To compensate for this inability to assay transcription
factor activity at the large scale, there have been several
efforts to infer regulatory networks computationally [7].
One of these approaches, called Network Component
Analysis (NCA), is a method for determining both
activities and regulatory influence for a set of transcrip-
tion factors with known target genes [8]. NCA has been
successfully applied in several areas. It was used to
identify previously unnoticed oscillatory activity patterns
in the yeast cell cycle [8], as well as to generate a
predicted activation time course of catabolite repressor
protein in Escherichia coli, which was verified experimen-
tally [9]. More recently, NCA was used to predict
activities of important transcription factors like sterol
regulatory element-binding proteins and peroxisome
proliferative-activated receptors in a mouse knockout
model of human glycerol kinase deficiency [10,11]. In
parallel, several studies have expanded and strengthened
NCA as a computational tool [12-14].

In eukaryotic systems, inflammation and activation of
innate immunity are fundamental host responses to
microbial invasion and endogenous danger signals.
Blood leukocytes contribute to this inflammatory response,
and exposure to a prototypical stimulus such as LPS leads
first to changes in gene expression, then production of
cytokines which are secreted and cause secondary tran-
scriptional and other responses [15]. In previous work, we
and others generated a set of gene expression profiles from
human subjects over 24 hours following the intravenous


administration of bacterial endotoxin LPS [16]. Experi-
mental endotoxicosis produces in the previously healthy
individual a transient but significant systemic inflamma-
tory response, characterized by fever, tachycardia, malaise,
and a hepatic acute phase response. Administration of
endotoxin is presumed to model the early inflammatory
changes associated with a microbial invasion, sepsis and
the systemic inflammatory response syndrome [17]. We
used this data to determine important clusters of genes
involved in the early inflammatory response, as well as to
depict the temporal changes in gene expression as
inflammation resolved over the first twenty-four hours.
In this study, we calculated transcription factor activities
and regulatory influences in the above dataset using NCA,
and interpreted the results to develop a dynamic network
of transcription events following experimental endotox-
icosis in humans.


Results and Discussion
Our approach follows the schematic in Figure 1. NCA
requires two inputs: a set of gene expression profiles and
a pre-defined regulatory network, which is a matrix that
contains initial estimates of the influence each transcrip-
tion factor on the target genes. The original gene
expression data set is obtained from Calvano et al [16],
in which peripheral blood leukocytes were obtained
from four different individuals prior to and at five time
points after injection with endotoxin, 24 profiles in total.

To define a regulatory network which could account for a
significant percentage of the gene expression response,
we identified a set of key transcription factors previously
known to be involved in the LPS response, together
with a set of known target genes for these factors. Ten
transcription factors were chosen for our study (listed
here by gene name for continuity). NFKB1 (encoding
p50/pl05), RELA (encoding p65) and IRF3 were chosen
as factors involved in the primary response to endotoxin.
Endotoxin binding to Toll-like receptors (TLR) leads to
activation of NF-KB dimers, among which p65:p50 is
common [18]. LPS stimulation also induces IRF3
activation through TLR4 [19]. These transcription factors
induce expression of several cytokines which can further
activate a secondary transcription response through
factors such as STAT1, 3 and 6 [15,20]. CREB1 is
activated by LPS through the p38 kinase-SAPK2 pathway
[21]. It is known that LPS activates AP-1 complexes
consisting of FOS, JUN, JUNB and JUND [22]. Among
these factors, JUN and FOS were chosen for our model.
The role of MYC in inflammation response is poorly
understood [23]; however, many genes connected to
MYC showed significant changes in their expression
levels in the original study and so we included it as
well [16].



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To identify established regulatory interactions between
these transcription factors and target genes, we relied
largely on the primary literature [15,16,20,22,24-28]
(Figure lA). However, two knowledge-bases were also
used: Ingenuity Systems http://www.ingenuity.com and
Pathway Studio [29]. Both the Ingenuity and Pathway
Studio knowledge-bases consist of regulatory relation-
ships parsed from MEDLINE abstracts; the Ingenuity
knowledge-base also includes information from manu-
ally-curated peer-reviewed publications. For our ten
transcription factors, this strategy resulted in a list of
1,287 target genes, with 2,183 interactions between
transcription factors and target genes. To reconcile
differences in these different sources of regulatory
network information (literature, Ingenuity, Pathway
Studio), we only included an interaction in our network


if it could be identified in two out of the three resources.
This filtering process reduced our list to 219 target genes
regulated by 306 interactions with the ten transcription
factors. To focus on the most useful expression informa-
tion, we only considered target genes for which expres-
sion changed significantly over time (p-value < 0.01).

The network for the inflammatory response finally
included 10 transcription factors, 99 target genes and
149 regulatory relations. This network can be repre-
sented in matrix form, with a density of ~15%, or 149
relations/(10 factors x 99 targets). In contrast, the
expected density of a genome-wide regulatory relation-
ship matrix, given our current state of knowledge about
human transcriptional regulation would be about 0.1%
(~20,000 relations in Ingenuity Systems and Pathway


Raw Data


Primary Ingenuity Pathway
Literature Systems Studio





2183 interaction links
1287 target genes


Link filter:_ found in >1
knowledge-bases


306 interaction links
219 target genes


Gene filter: significant
expression change
(p < 0.01)

149 interaction links
99 target genes


MicroarrayGene
Expression Data [E]


Regulatory Network [S(o)]
TF1 TF2 TF3



"0 '


NCA Algorithm


[E] [S.[A]

A(k) +- arg minE -S(k-1)A 2


S(k) +- argminE SA(k) 2 + 2 S 2
s /

Numerical Results V


Figure I
Schematic of the approach. (A) Flowchart describing the steps to reconstruct our initial transcriptional regulatory
network. (B) A set of gene expression profiles (matrix E) and about a proposed structure for the underlying transcriptional
regulatory network (matrix S(u)) are used as inputs for Network Component Analysis (NCA). NCA uses an algorithm
that first calculates the expected transcription factor activities (matrix A), and then recalculates S based on the new values
of A, until both matrices converge. The outputs of this procedure are A* and S*, final values of A and S, which provide
information about transcription factor activity as well as regulatory structure, respectively.


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Studio databases, ~1,000 transcription factors and
~20,000 target genes). Our network density is therefore
relatively high, reflecting the comparatively high level of
research interest in this system.

We estimated the activation of the transcription factors
in our network over time using NCA (Figure iB). NCA
decomposes a matrix containing gene expression values
(E) into a matrix which represents the influence of a
transcription factor on a target gene (strength matrix S)
and a matrix which contains the transcription factor
activities (activity matrix A) [8]. We found that both
outputs of NCA predicted factor activities A and
regulatory influences S have added additional insights
to gene expression data where the underlying regulatory
network structure is partially known.


Transcription factor activities
Figure 2A and 2B show the estimated activities of our
10 transcription factors. Transcription factor activities
clearly showed early-, mid-, and late-phase action in
response to LPS. IRF3, NFKBI(p50/pl05) and RELA
(p60) were activated within 2 hours after the endotoxin
was injected. IRF3 activation peaked at 2 hours and
returned to its base level at 4 hours. NFKB1 and RELA
were also activated early but decreased in activity more
slowly. These three factors can induce expression of
tumor necrosis factor alpha, which then further activates
the NF-KB complex [25,26], and could contribute to the
extended NF-KB activation. JUN and FOS are known to be
activated through the JNK pathway [30,31], and had a
similar activation profile to NFKB1 and RELA. In contrast,
STAT1, STAT3 and CREB1 exhibited a late-phase response.
The STAT1 and STAT3 predictions correspond to previous
findings that STATs are activated by cytokines transcribed
by the NF-KB complex [15]. It was surprising that predicted
CREB 1 activation peaked at four hours, given that previous
reports detect phosphorylated CREB at 30 minutes [32].
However, the prediction was the result of late-phase
induction of known CREB-dependent gene expression,
such as ALAS1 and CEBPD [33,34]. Both STAT6 and MYC
were predicted to be somewhat deactivated over nine
hours. Deactivation of STAT6 was predicted due to
repression of MHC-II class genes which are known to be
regulated by STAT6 [35], as well as the expression of
SOCS1, which has been reported to lead to deactivation of
STAT6 [36]. MYC expression can be decreased through a
STAT1-dependent pathway under IFN-y stimulation con-
ditions [37], and it is possible that the deactivation
predicted here depends on STAT1 as well.

Transcription factor activities are sometimes, but not
always, correlated with the gene expression of the
factor. We compared the calculated transcription factor


activities with the gene expression data for each factor
(Figure 2B). NFKB1, RELA, STAT1, STAT3 and MYC
showed strong positive correlation between activities
and expression (correlation coefficient c > 0.56),
possibly due to auto- or cross-regulation. For example,
NFKB1 activity and expression are tightly correlated (c =
0.6022), possibly because the NF-KB p65:p50 complex
can regulate NFKB1 [38,39]. STAT1 activity and expres-
sion are also strongly correlated (c = 0.8362), which
might relate to the transcriptional effect of STAT3 on
STAT1 expression [40], particularly given that STAT1 and
STAT3 have highly correlated activities (Figure 2C, c =
0.9329). On the other hand, the activities and expression
show lower or no correlation for IRF3, JUN, FOS and
CREB (-0.15
The linear model of gene expression upon which NCA
rests does not account for the interactions between
transcription factors. However, we wondered if the NCA-
predicted correlation in transcription factor activities
could be due to the combined action of two transcription
factors, either as a complex or otherwise. We therefore
checked transcription factor pairs with significant activity
correlation to published protein-protein interactions
catalogued in the Biomolecular Interaction Network
Database (BIND) [41]. Interestingly, transcription factors
known to act together showed high correlation in their
activity profiles (Figure 2D). For example, highly corre-
lated transcription factors NFKBI(p50/pl05) and RELA
(p65) regulate their target genes as a p65:50 heterodimer
form [42], and STAT1 and STAT3 are also known to act
as a dimer [20], as are JUN and FOS [30]. Additionally,
some transcription factors (STAT1 and CREB1, STAT6 and
MYC) showed a positive correlation in their activity even
though they are not known to form a complex with
other transcription factors. Transcription factors can have
similar and even coordinated activities without direct
interaction, so it may be that these latter predictions
reflect an indirect interaction.

On the other hand, it is possible that some of the
correlated transcription factor activities may be based on
incorrect NCA predictions. The largest possible source of
error for NCA decomposition is the initial connectivity
matrix, which is based on the current, generally incomplete
or erroneous, understanding of the human transcriptional
regulatory network. The effect of missing or false data in
the connectivity matrix is hard to predict in advance.
However, the sensitivity of NCA to the connectivity matrix
can be estimated by adding or removing connections
randomly from the original matrix, and repeating the NCA
calculation multiple times [14]. Using this approach, we
found that transcription factor activities predicted by NCA
and our original connectivity matrix were robust, even if
10-15% of the connectivity matrix contained inaccurate


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B
2 1.5 1.5 1 1
c=-0.1514 A c=0.6022 c=05647 0.5 c
1 05 1 0.5
0. 0.5

0 h -. P.^c=0.8362 c= 0.6584
-1 -0.5 0 1 -0.5
0246 9 24 0246 9 24 0246 9 24 0246 9 24 0246 9 24
IRF3 NFKB1 RELA STAT1 STAT3


STAT6


0246 9
CREB1


STATIC 'RF
NFKB1
RELA
STAT3


STATS 6
CREB1


0 2 4 6 9 24

-25 0 2.5


15 c=0.2293 5 c=0.3669


05
0.5
0

0246 9 24 0246 9 24
JUN FOS


0246 9
MYC
- Activity
- Expression


Figure 2
Transcription factor activities calculated using NCA. (A) Predicted activities of the ten transcription factors used in this
study. For each transcription factor, rows represent progression in time and columns correspond to the four human subjects.
Activities of each row are normalized to the zero time point. (B) Transcription factor activities (blue) compared to gene
expression (green), with Pearson correlation coefficients noted. Both activity and expression at each time point are averages
normalized to the time = 0 values, and the activity is further scaled for direct comparison with the expression values.
(C) Correlation matrix between transcription factor activities. Red represents positive correlation, and blue represents
negative correlation. (D) Inferred combinatorial regulation pairs of transcription factors. A blue solid line indicates that
the pair was supported by protein-protein interaction knowledge of BIND and high correlation of their activities (>0.75).
A black solid line indicates that the pair was only supported by high correlation, and a blue dotted line indicates that
the pair was only supported by the interaction database.


connections (Table 1). Given that our matrix was limited
to only high-confidence interactions, this level of sensitivity
was assumed to be tolerable.

Regulatory influence matrix and gene expression
clustering
We thought that the adjusted strength matrix might be
used to enhance typical gene expression clustering


techniques. Signed quantitative values of the adjusted
strengths were able to form more biological meaningful
clusters beyond the prior binary regulatory connections. In
Figure 3A, target genes were hierarchically clustered with
the adjusted strengths of transcription factors and shown
with gene expression. We identified seven major dusters,
which correlate to the coordinated action of transcription
factors to regulate gene expression. Cluster A highlights the


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FOS


(STAT6) CSTAT3)_ NFKB1



,---,-1- RELA)
(MYC) STAT1I. -^

CREB1


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Table 1: NCA simulation with random noisy connections

1% 5% 10% 15% 20%
Mean(Corr) 0.9892 0.9548 0.9330 0.9107 0.8880
SD(Corr) 0.0678 0.1258 0.1378 0.1579 0.1662

NCA simulation was performed based on the original network model
with 1-20% of random noisy connections. A pair of a transcription
factor and a target gene was randomly selected; a random connection
was added if the original connection did not exist, or removed
otherwise. For each percentage of random connections, the simulation
was repeated by 100 times. Mean and standard deviation of activity
correlations with the original noise-free network model were
calculated.


influence of NFKBl(p50/pl05) and RELA(p65) on a set of
eighteen genes. Interestingly, some genes are linked to p65
only, suggesting that these genes may be under the specific
control of the p65:p65 homodimer, rather than the p65:
p50 heterodimer. For example, the duster suggests that
CXCL10 expression depends on both p65 and p50, which
has been demonstrated experimentally in NFKB1-/ and
RELA-- knockout mice [43]. Clusters B and C contain the
genes regulated by STATs 1 and 3, while Cluster D genes
are regulated by JUN and FOS. Clusters E and G are
primarily regulated by MYC, but with repression in E and
induction in G. Cluster F genes are regulated by STAT6. All
of the transcription factors known to act in dimers
[20,30,44] the NF-KB complex of NFKB1-RELA, as well
as STAT1-STAT3 and JUN-FOS were either in the same
cluster or closely adjoining clusters, and had correlated
activation profiles. However, although STAT6 and MYC
had correlating activation profiles, the genes under their
influence (Clusters E, F and G) did not cluster closely.
Therefore, when studied together, activation profiles and
regulatory influences may provide insight on the coordina-
tion between transcription factors.

Although our clustering was based on the matrix of
regulatory influence, the clusters also provided a strong
basis for interpreting gene expression. Pair-wise correla-
tion tests on expression between genes within a cluster
showed significantly higher average correlation than
random clusters (Table 2). Furthermore, the resulting
gene expression clusters can be immediately linked to
the specific transcription factors whose action created the
expression profile. Importantly, clustering by transcrip-
tion strength can identify new clusters unobtainable by
clustering the expression data alone. For example,
Cluster F and G could not be distinguished when the
same clustering method was applied to the gene
expression data alone (Figure 3B). However, they formed
unmistakable clusters from the regulatory strength
matrix, being linked to the regulatory influence of either
STAT6 or MYC. Furthermore, our clusters required the
NCA-processed strength matrix, and could not be


obtained from the initial connectivity matrix, the
clustering of which led to groups of genes that did not
show common expression patterns (Figure 3C). We
conclude that the estimated transcription factor regula-
tory strengths can provide unique insights with regard to
the regulation underlying gene expression, even between
genes with similar expression.


Correlation test and prediction over extended
regulatory sets
The clusters shown in Figure 3A suggested that we might
be able to use our cluster information to discover new
regulatory relationships. We first determined the average
normalized expression pattern of the genes in each cluster
(= model gene group). The expression vector for each
gene was normalized to have zero mean and a standard
deviation of 1, and then normalized gene expression sets
were averaged for each cluster (Figure 4A). We then
divided all human genes measured on the expression
array into three groups: those for which we had high-
confidence regulatory information linking the dominant
transcription factors in the cluster to the gene (model
genes); genes for which we had lower-confidence regula-
tory information (found in only one of the two knowl-
edge-bases), but could still be valid to extend our model
(extended genes); and genes where we found no evidence
of regulation by the cluster transcription factors (no-
evidence genes). If a cluster had more than two dominant
transcription factors, only genes which had established
regulatory interactions with all factors were collected for
the extended gene group.

We first wanted to see if a gene in the extended gene
group had similar expression to a cluster (Figure 4B).
First, Pearson's correlations were calculated between
each gene in the extended gene group and the average
normalized gene expression of each cluster. We then also
randomly selected one thousand genes from the no-
evidence gene group, and calculated correlations
between expression of these genes and the clusters. To
obtain standard deviations, we performed this step one
hundred times. The fraction of genes with a Pearson's
correlation > 0.5 was then compared between both
groups using Fisher's exact test (Figure 4C). We found
that average gene expression in each cluster was more
highly correlated with genes in the corresponding
extended gene set than in the no-evidence gene set,
particularly for Clusters A, B and G.

Based on an earlier report involving p53 targets [45], we
decided to use the average normalized expression pattern
of each cluster to predict new target genes for dominant
transcription factors, First, we identified the genes with
significant changes in gene expression in each gene



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Adjusted Strength Expression


SI i(A)


-

I (B)

H a. (C)



















IRF3 RELA STAT3 CREB1 FOS Oh 2h 4h 6h 9h 24h
NFKB1 STAT1 STAT6 JUN MYC
-1 1 5 -3 0 3


Expression


Connectivity Expression


..Kv


Oh 2h 4h 6h 9h 24h


Figure 3
Hierarchical clustering in the context of a defined regulatory network. (A) The adjusted strength matrix was used
for clustering, after which the gene expression matrix was appended. Seven major clusters which have more than five
associated genes are highlighted. In the adjusted strength matrix heatmap, green color indicates that there is no prior
regulatory connection in our model while white color indicates a weak regulatory influence. (B) Clustering with gene
expression only. Genes in the Cluster F(regulated by STAT6) were noted with green dots, and genes in the Cluster G
(regulated by MYC) were noted with orange dots. (C) Clustering with the binary regulatory relations (initial connectivity
matrix) assuming all regulatory strengths are equal.


Table 2: Major clusters formed from the adjusted strength matrix

Cluster Num. of genes Dominant transcription factors Avg. of pair-wise correlations P-value
A 18 NFKBI, RELA 0.5742 <0.001*
B II STATI 0.7815 <0.00 1*
C 5 STAT3 0.5138 0.025
D 17 JUN, FOS 0.4054 0.001
E 6 MYC 0.4350 0.031
F 5 STAT6 0.7546 0.004
G 21 MYC 0.6442 <0.001*

For N genes in a cluster, N(N-1)/2 pair-wise Pearson's correlations on expression were measured and their average was calculated. Next, N genes
were randomly selected from a set of 18,000 human genes and the average pair-wise correlation was calculated from the random gene set.
The distribution of the average pair-wise correlation of random genes was re-estimated 1,000 times to generate a null distribution of the
average pair-wise correlations. A p-value for each cluster was estimated by counting the number of random gene sets for which the average
correlation is larger than the cluster correlation. *No random gene set that exceeded the cluster was found in 1,000 repeats


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G
F-


Time


0.05 0.1 0.15 0.2 0.25
Accepted rate for "no evidence" gene set


A B D



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B j~r-m-IrI


N
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Figure 4
Identification of new target genes for major clusters. (A) The average expression profiles of the four clusters with > 10
members. (B) Expressions of extended regulatory genes sorted by correlation coefficients(c) with the average expression
profile of a cluster. Each extended gene group was divided into highly correlated (c > 0.5), un-correlated (t0.5 anti-correlated (c < t0.5) groups. The average gene expression of each cluster is shown as a row at the top of each column.
(C) Ratio of highly correlated genes (c > 0.5) in the sets of extended regulatory genes and 1,000 randomly chosen genes. Error
bars were calculated as the standard deviation of a population derived from 100 repeated tests. P-values measured by the
Fisher's exact test are noted above each column set. (D) Fraction of acceptable new predicted cluster genes from both the
extended and "no evidence" gene sets. Significantly expressed genes (p < 0.01) in both sets were plotted against each other
using a range of Pearson's coefficient cutoff values for Clusters A, B, D, and G. The dashed line indicates where the fraction of
acceptable genes is equal from both the extended and "no evidence" sets


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group (p < 0.01). We then identified the subset of genes
whose expression best matched each cluster using
Pearson's correlations, and determined the relationship
between the fraction of accepted genes (based on a range
of cutoffs) that was contained in the extended gene
set versus the "no evidence" gene set for each cluster
(Figure 4D). As expected, all extended gene sets had
higher accepted rates than the "no evidence" gene sets.
However, as can be seen in Figure 4D, genes in the
extended set for Clusters A and B were many times more
likely to be matched the cluster aggregate expression
profile than "no evidence" genes. This indicates that
Cluster A and B expression profiles are better able to
distinguish true member genes than the profiles for
Cluster D or G. We identified 12 genes in the extended
gene set for Cluster A and 24 for Cluster B that were
highly correlated (c > 0.5) to the cluster aggregate
expression profile (Table 3).

We also focused on Clusters A and B for predicting new
target genes from the "no evidence" group. Some of the
predicted new member genes for these clusters are listed
in Table 4 [46-48]. Although there was no evidence for
including these genes in our model initially, we were
able to partially validate certain target gene predictions
based on evidence beyond the original knowledge-bases
that we used to define our sets. Notable among this
evidence was the use of genome-scale location analysis
[46], as well as bioinformatics techniques [47] to detect
NF-KB binding to the promoters of several predicted
target sites. We conclude that such clustering may be
useful for identifying new target genes, particularly in
combination with other methods.


Overall regulatory dynamics in response to LPS
Finally, we were able to address our original goal of
building an integrated temporal model of the human
blood leukocyte response to LPS (Figure 5). This
required the integration of our calculated transcription
factor activities, transcription factor regulatory influences
on each gene, clustering on the adjusted strength, and
the gene expression data. Endotoxin was administered to
the subjects at 0 hours. During the next two-hour period,
IRF3, p65 and p50 were activated and interacted to
regulate gene expression, as were JUN and FOS as well as
CREB1. By two hours, these transcription factors had
already affected gene expression, including the genes in
Clusters A and D as well as the additional genes we
predicted to belong in Cluster A. Between 2 and 4 hours
after endotoxin administration, cytokines such as the
interleukins (ILs) and tumor necrosis factor (TNF) whose
genes were expressed at 2 hours were produced and
secreted. These secreted proteins then and maintained or
initiated the activity of several transcription factors in


Table 3: Predicted genes for Cluster A and B from the extended
gene sets

Cluster Gene Symbol Gene Name

A CFLAR CASP8 and FADD-like apoptosis regulator
CXCL I chemokine (C-X-C motif) ligand I
EFNAI ephrin-AI
GOS2 GO/G I switch 2
ILI RI interleukin I receptor, type I
ILIRN interleukin I receptor antagonist
OLRI oxidized low density lipoprotein (lectin-like)
receptor I
PLK3 polo-like kinase 3
PTX3 pentraxin-related gene, rapidly induced by
IL-I beta
RUNXI runt-related transcription factor I
TNFAIP6 tumor necrosis factor, alpha-induced protein 6
TNIPI TNFAIP3 interacting protein I

B CASP4 caspase 4, apoptosis-related cysteine peptidase
CD 14 CD 14 molecule
CISH cytokine inducible SH2-containing protein
GBPI guanylate binding protein I,
interferon-inducible, 67 kDa
GBP2 guanylate binding protein 2,
interferon-inducible
GCH I GTP cyclohydrolase I
HSPAIA heat shock 70 kDa protein IA
IF116 interferon, gamma-inducible protein 16
IFIT3 interferon-induced protein with
tetratricopeptide repeats 3
IFITM I interferon induced transmembrane protein I
IGF I R insulin-like growth factor I receptor
ILIORB interleukin 10 receptor, beta
IRF2 interferon regulatory factor 2
ISG 15 ISG 15 ubiquitin-like modifier
ISG20 interferon stimulated exonuclease gene 20 kDa
JAK2 Janus kinase 2
MXI myxovirus (influenza virus) resistance I,
interferon-inducible protein p78
PLSCR I phospholipid scramblase I
RIPKI receptor (TNFRSF)-interacting serine-
threonine kinase I
SOCSI suppressor of cytokine signaling I
STAT2 signal transducer and activator of
transcription 2
TIMPI TIMP metallopeptidase inhibitor I
USP18 ubiquitin specific peptidase 18
WARS tryptophanyl-tRNA synthetase

Genes that showed high correlation to the average expression profile of
a cluster (c > 0.5) were accepted as part of the cluster.

the blood leukocytes. Presumably, TNF then reactivated
the NF-KB complex and some of ILs stimulated AP-1
complex [49,50]. In contrast, IRF3 activation rapidly
returned the base level of activity. The ILs could have
activated the STATs to initiate a secondary response,
inducing expression of the genes in Clusters B and C
together with the additional genes predicted to belong
to Cluster B. After 4 hours, the transcription factors
began to return to their basal level of activity, leading to
a near-complete return to initial values of gene expres-
sion by 24 hours. The temporal model therefore



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Table 4: Predicted genes for Cluster A and B form the "no evidence" group


Gene Symbol

ETS2
MTFI
SAMSN I
IVNSIABP
IFNGR2
PLAUR
IL IR2
AZIN I
EHDI
PCNX


Gene Name


v-ets erythroblastosis virus E26 oncogene homolog 2
metal-regulatory transcription factor I
SAM domain, SH3 domain and nuclear localization signals I
influenza virus NSIA binding protein
interferon gamma receptor 2
plasminogen activator, urokinase receptor
interleukin I receptor, type II
antizyme inhibitor I
EH-domain containing I
pecanex homolog


Related to innate immunity IFNGR2 interferon gamma receptor 2
ILI R2 interleukin I receptor, type II
MAP4K4 mitogen-activated protein kinase kinase kinase kinase 4
NCF I neutrophil cytosolic factor I
TXN thioredoxin
RIPK2 receptor-interacting serine-threonine kinase 2


Supported by other evidence


Top 10


IFNGR2*
GPR84**
FCAR**
GADD45B**

TRIMS
GK
SPI 10
TMEM 140
CHMP5
RHBDF2
SAMD9
DDX58
TLE3
TRIM21


interferon gamma receptor 2
G protein-coupled receptor 84
Fc fragment of IgA, receptor for
growth arrest and DNA-damage-inducible, beta

tripartite motif-containing 5
glycerol kinase
SPI 10 nuclear body protein
transmembrane protein 140
chromatin modifying protein 5
rhomboid 5 homolog 2
sterile alpha motif domain containing 9
DEAD (Asp-Glu-Ala-Asp) box polypeptide 58
transducin-like enhancer of split 3
tripartite motif-containing 21


Related to innate immunity CSF2RB colony stimulating factor 2 receptor, beta, low-affinity
HCK hemopoietic cell kinase
ILl3RAI interleukin 13 receptor, alpha I
TLRI toll-like receptor I
TNFRSFIA tumor necrosis factor receptor superfamily, member IA


Supported by other evidence


DDX5***
H ERC5***
lFIT2~**
OASL~**
SNX 10***
ACSL4***


DEAD (Asp-Glu-Ala-Asp) box polypeptide 5
hect domain and RLD 5
interferon-induced protein with tetratricopeptide repeats 2
2'-5'-oligoadenylate synthetase-like
sorting nexin 10
acyl-CoA synthetase long-chain family member 4


The ten most correlated target genes, as well as genes related to innate immunity (from the fifty most correlated genes) and genes which were
supported by other evidence, are shown. *Predicted as a target gene from the motif sequence analysis. **DNA-protein binding detected in LPS
stimulation. ***Up-regulated by a mutant of STATI which can activate expression in the absence of tyrosine phosphorylation.


provided a global view of activation, transcription and
resolution of the blood leukocyte response to lipopoly-
saccharide in humans.



Conclusion
The overall goal of this work was to build a dynamic
network of transcription events following endotoxin
administration from the time course response by global
gene expression in peripheral blood leukocytes. From


the expression profiles, we were able to predict the
activities of ten transcription factors over time, as well as
the regulatory strength a given transcription factor
exerted on its target genes using NCA. Taken together,
the activities often exhibited a high degree of correlation,
both between factors and also between a factor's activity
and its gene expression profile.

We also found that the regulatory strength matrix can be
clustered to determine groups of genes which are not only



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Cluster


Category

Top 10


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Oh A)
- Activation


24h 9h






Li 5;ii*t* -


6h


S. **
"n--.i .00 '
-0 y '" -^i 1*
r^#4^4t 0
W ^f ^ ^


4-
V


-3 0 3
Figure 5
A dynamic network of transcription. At time zero, LPS is injected, giving rise to transcription factor activation, which then
leads to induction or repression of gene expression, production and secretion of cytokines, and initiation of secondary signals.
Target genes which correspond to secreted proteins (e.g., ILIO, ILIA and ILIB) are noted with green circles, and transcription
factors that are regulated by other factors, such as STAT I and MYC, are noted with cyan circles. The seven major clusters marked
in Figure 3A are grouped with orange boxes. Black lines denote activation of a transcription factor by an extracellular signal, red and
blue lines show the influence of a transcription factor on a target gene, and green dotted lines indicate secretion of a gene product.


co-expressed, but also co-regulated. Importantly, new and
biologically relevant clusters were determined, suggesting
that clustering by this approach is potentially more
meaningful than methods which do not incorporate
regulatory network information. Identification of these
clusters also led us to identify many additional putative
interactions between transcription factors and target genes
not included in the known network, and most importantly,
enabled us to describe and visualize the activation of
regulatory proteins and target genes over time.

Certain limitations in both the available expression data
as well as NCA itself could be addressed to make this
approach more powerful. Gene expression analyses
obtained from whole blood leukocyte samples provide


an integrated signal from different leukocyte populations
which are difficult to deconvolute, and so using a single
cell population would be advantageous, such as could be
obtained using cell sorting or other methods. Addition-
ally, the number of transcription factors which can be
used in NCA is approximately the number of expression
profiles in the data set, and so a greater number of
expression profiles obtained at best shortly after the
endotoxin administration would also have been
useful. Finally, NCA's scaling property, which makes it
difficult to predict the direction of transcription factor
activity, as well as NCA's current inability to incorporate
time course information from the data set are important
limitations to the method. Some approaches that may
overcome these challenges include recent studies in


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2h


4h


..........
- -
. . . .


H


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which transcription factor activities were estimated using
ordinary differential equation [45] or probabilistic
models [51,52] of time course data. Future work might
therefore focus on combining NCA with such efforts.

Notwithstanding these limitations, we were able to
reconstruct the dynamics of endotoxin-dependent tran-
scription in human peripheral blood leukocytes using
the above results. This included identifying the activity of
ten transcription factors regulating expression of ninety-
nine genes. We also were able to identify additional
genes that could be included in our model, notably 36
which had less initial evidence, but were substantiated
by our predictions. Given that there were 1,215 genes
with significant changes in gene expression for which
regulatory relations were known, we were therefore able
to capture between 8% (= 99 initial model genes/1,215
genes with significant expression changes and known
regulatory relations) and 11% (99 + 36 additional
genes = 135/1,215) of the explainable response. Further-
more, we were also able to identify new target genes
based on the average gene expression profile of
significant clusters, which could expand the scope of
our temporal network still further. With a larger network
reconstruction and data set specifically designed for use
with NCA, it might be possible to move toward a near-
complete characterization of dynamic transcription
responses.


Methods
Data preprocessing and statistical analysis
To process our gene expression dataset prior to NCA, the
log2 ratio of post-injection time points to the pre-
injection time point was calculated. The significance of
expression changes was then tested using one-way
ANOVA, where the null hypothesis was that average
gene expression levels were the same for each time point.
We selected genes for our model if the ANOVA p-value
was less than 0.01. Among 18,398 genes in the dataset,
5,518 genes were determined to be induced or repressed
significantly. 1,215 of the genes that experienced a
significant change in expression also had information
about their regulation in the knowledge-bases we used.

Network component analysis
NCA was developed by James Liao and colleagues [8].
Briefly, NCA models the expression of a gene as a linear
combination of the activity of each transcription factor
that controls the expression of the gene. Using this
framework, NCA can estimate transcription factor
activity and regulatory influence from a given regulatory
network and a set of gene expression data. We followed
the established method for generalized NCA, using a
regularization factor of 0.8 to regulate the strength


matrix S [13]. One important modification we made in
our implementation of NCA was to normalize the
transcription factor activity matrix. At each iteration
step, A was normalized so that the norm of each row was
1. The S matrix was then also scaled as follows:

S.j -- S. A. 1 A. -- A ./I Aj .

where S.j and Aj. represent the jth column of S or row of
A, respectively. This normalization stabilizes the calcula-
tion by preventing too large or too small values of A, but
has no effect on the overall results due to NCA's scaling
property [8].

Hierarchical clustering on adjusted strength matrix
Having determined S using NCA, we wanted to use it for
clustering genes. The first step was to enable comparison
of transcription factor strengths to each other. A main
challenge in such a comparison is that because of the
scaling property of NCA [8], Sij and Ajk are not unique
solutions. However, the product SyAjk is unique. There-
fore, in order to enable clustering of the regulatory
influences, we generated an adjusted strength matrix
which is constant regardless of strength and activity. This
matrix is calculated as follows:


S (ISj Aj.,Ei.)
(Ei.,Ei.*)

where <,> represents the inner product of two vectors.
We used hierarchical clustering to divide the adjusted
strength matrix into meaningful clusters using angle
cosine as the distance metric.

The dominant transcription factors associated with each
cluster can be readily determined visually in this case.
However, we also used a computational method to
identify these dominant factors. This was accomplished
by calculating a contribution factor for each transcription
factor in a cluster. The contribution factor of transcrip-
tion factor for cluster C was calculated as the fraction of
influence a given transcription factor imposed on the
cluster with respect to the total influence of all L
transcription factors, as follows:


2


xfric iielS~


Transcription factors for which the contribution factor
was larger than 0.2 were chosen as dominant transcrip-
tion factors of the cluster.


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Authors' contributions
JS designed the project, performed the analysis, and
drafted the manuscript. WX provided the data, guided
the analysis, and helped to draft the manuscript. LM
revised the manuscript critically. RD conceived the study,
and guided the research. MC supervised all aspects of the
project. All authors read and approved the final manu-
script.


Acknowledgements
This work was funded by the NIH through the Inflammation and the Host
Response to Injury research project (U54-GM62 119), as well as a K99/ROO
award to MWC (CA 125994-01 Al). We thank Jonathan Karr and Robert
Winfield for critical reading of the manuscript. The following is a list of
participating investigators of the Inflammation and the Host Response to
Injury research project: Henry V. Baker, Ulysses GJ. Balis, Paul E. Bankey,
Timothy R. Billiar, Bernard H. Brownstein, Steven E. Calvano, David G.
Camp II, Irshad H. Chaudry, J. Perren Cobb, Joseph Cuschieri, Ronald W.
Davis, Asit K. De, Celeste C. Finnerty, Bradley Freeman, Richard L.
Gamelli, Nicole S. Gibran, Brian G. Harbrecht, Douglas L. Hayden, Laura
Hennessy, David N. Herndon, Marc G. Jeschke, Jeffrey L. Johnson,
Matthew B. Klein, James A. Lederer, Stephen F. Lowry, Ronald V. Maier,
John A. Mannick, Philip H. Mason, Grace P. McDonald-Smith, Carol L.
Miller-Graziano, Michael N. Mindrinos, Joseph P. Minei, Lyle L. Moldawer,
Ernest E. Moore, Avery B. Nathens, Grant E. O'Keefe, Laurence G.
Rahme, Daniel G. Remick, David A. Schoenfeld, Michael B. Shapiro,
Geoffrey M. Silver, Richard D. Smith, John D. Storey, Robert Tibshirani,
Ronald G. Tompkins, Mehmet Toner, H. Shaw Warren, Michael A. West,
Rebbecca P. Wilson, Wenzhong Xiao.


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components.] Biol Chem 2007, 282:11230-7.
52. Sanguinetti G, Lawrence ND and Rattray M: Probabilistic "BioMed Central will be the most significant development for
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