GIW2008, Gold Coast, Australia, 1-3 December 2008

Modeling genome-wide humanregulatory network initiated by TFs
and miRNAs through forward andreverse engineering
Yixue Liyxli@sibs.ac.cn

Shanghai Center for Bioinformation Technology
Key Laboratory of Systems Biology
Chinese Academy of Sciences

Contents in the topic
 Some Definitions
 Our methodology
 Multiple linear regression model Topological analysis of the network

Some Definitions
 Biological networks
 microRNA Forward and reverse engineering

1. Biological network
In a topological sense, a network is a set of nodes and a set of
directed or undirected edges between the nodes. Biologicalnetworks using such computational inference methods include:
1. Transcriptional regulatory networks. Genes are the nodes
and the edges are directed. A gene serves as the source of adirect regulatory edge to a target gene by producing an RNAor protein molecule that functions as a transcriptional
activator or inhibitor of the target gene. Computationalalgorithms used to infer the topology take as primary input
the data from a set of microarray runs measuring the mRNA
expression levels of the genes under consideration forinclusion in the network.

Transcriptional regulatory networks

In the field of molecular biology, a transcription factor (sometimes called asequence-specific DNA binding factor) is a protein that binds to specific parts of
DNA using DNA binding domains and is part of the system that controls the transfer(or transcription) of genetic information from DNA to RNA. Transcription factors
perform this function alone, or by using other proteins in a complex, by increasing(as an activator), or preventing (as a repressor) the presence of RNA polymerase, theenzyme which activates the transcription of genetic information from DNA to RNA.

Transcriptional regulatory networks. Genes are the nodes and the edges are directed.
A gene serves as the source of a direct regulatory edge to a target gene by producing an
RNA or protein molecule that functions as a transcriptional activator or inhibitor of thetarget gene. If the gene is an activator, then it is the source of a positive regulatory
connection; if an inhibitor, then it is the source of a negative regulatory connection.

Biological network
2. Signal transduction networks(very important in the
biology of cancer). Proteins are the nodes and the edges
are directed. Primary input into the inference algorithm
would be data from a set of experiments measuring
protein activation / inactivation (e.g., phosphorylation/dephosphorylation) across a set of proteins.
3. Metabolic networks. Metabolites are the nodes and theedges are directed. Primary input into an algorithm
would be data from a set of experiments measuringmetabolite levels.

Signal transduction networks, created by Roadnottaken

Chemokine receptors(GPCR)interact withchemokine, and trigger
a flux in intracellular
calcium ions signaling.
This causes cell
responses.

Signal transduction networks, created by Roadnottaken

Receptor tyrosine kinases(RTK) have been shown to be
not only key regulators ofnormal cellular processes but
also to have a critical role inthe development andprogression of many types ofcancer.

Signal transduction networks, created by Roadnottaken

Signal transduction refers to any process
by which a cell converts one kind of signal
or stimulus into another. Most processesof signal transduction involve orderedsequences of biochemical reaction inside
the cell, which are carried out by enzymes,
activated by second messengers, resulting
in a signal transduction pathway.

Signal transduction networks

A

Regulatory
subunits

R

A

A
A

A

C

A

PKA

R

A

C

Catalytic
subunits

Nucleus

A

C

R
R

C

CREB
P

CREB
DNA

Active kinase

cAMP

Activation

C
Gene
expression

ON

Alberts et al (2002) Molecular Biology of the Cell (4e) p. 857, 858

cAMP Controls Activity of Protein Kinase A

Kinase (PKA) presents in avariety of tissues. Changes
A
Activekinasein intracellular cAMPlevelsAcAMPA
can regulate cellularA
responses by influencing
A
Cinteraction
between theR
Regulatory(R) and CatalyticA(C) Subunits of PKA.
A

Regulatory
subunits

R

C

PKA

R

C

Catalytic
subunits

Nucleus

A

R

C

CREB
Activation

P

C
Gene
expression

CREB

ON

DNA

subunits

Nucleus

A
A
A
A

C

R
R

C

CREB
P

CREB
DNA

Active kinase

Activation

C
Gene
expression

ON

Alberts et al (2002) Molecular Biology of the Cell (4e) p. 857, 858

cAMP Controls Activity of Protein Kinase A

The PKA exists as aninactive tetrameric complex
A
Regulatory(R2C2),
which consists of
A asubunitscAMP
A
regulatory
dimer (R2)A
associated with two Catalytic
Subunits. WhenC cAMP binds
R
to R2, the tetramer
dissociates, forming R2 •cAMP4
and twoC
R complex
Catalytic
active Catalytic Subunits.

Alberts et al (2002) Molecular Biology of the Cell (4e) p. 857, 858

cAMP-DependentcAMP Controls Activityof ProteinProtein
Kinase AA

Regulatory
subunits

A

A
A

cAMP

R

C

A

R

C

A

A
Catalytic
subunits

Nucleus

R

CREB
P

CREB
DNA

A

R

ActiveCatalytic
Active kinase
Subunits can thenphosphorylate a wide
variety C
of intracellulartarget proteins, andswitch on a lot of genetranscriptionalC
regulatory processes

Activation

C
Gene
expression

ON

Metabolic networks, Glycolysis / Gluconeogenesis reference
metabolic pathway from KEGG.

Alberts et al (2002) Molecular Biology of the Cell (4e) p. 857, 858

cAMP Controls Activity of Protein Kinase A

Cartographic representation of the metabolic network for E.coli. Each circle
represents a module and is coloured according to the KEGG pathway
classification of the reactions belonging to it, while the arcs reflect the connection
between clusters. The area of each colour in one circle is proportional to thenumber of reactions that belong to the corresponding metabolism. The width of anarc is proportional to the number of reactions between the two corresponding
modules. For simplicity, bi-directed arcs are presented by grey edges. Jing et.al.,
BMC Bioinformatics 2006, 7:386.

Biological network
5. Intraspecies or interspecies communication networks inmicrobial communities. Nodes are excreted organic
compounds and the edges are directed. Input into an
inference algorithm is data from a set of experiments
measuring levels of excreted molecules.
6. Protein-protein interaction networks are also undervery active study. However, reconstruction of these
networks does not use correlation-based inference in thesense discussed for the networks already described(interaction does not necessarily imply a change in proteinstate)

Intraspecies or interspecies communication networks
The quorum-sensing paradigm in Gram-negative and positivebacteria

The Las, Rhl and Qsc quorum-sensing systems in Pseudomonas aeruginosa:
hierarchies and integration into cellular control circuits. For each circuit in the cell theinteractions between the different QS systems are indicated by arrows. Black arrows indicate
positive regulation and red arrows indicate negative regulation. Signals from the environment,
the intracellular metabolic status of the cell and other regulators, such as RpoS, RsmA andf

Intraspecies or interspecies communication
networksBlack arrows
indicate
positive
regulation

The Las, Rhl and Qsc quorum-sensing systems in Pseudomonas aeruginosa:
hierarchies and integration into cellular control circuits. For each circuit in the cell theinteractions between the different QS systems are indicated by arrows. Black arrows indicate
positive regulation and red arrows indicate negative regulation. Signals from the environment,
the intracellular metabolic status of the cell and other regulators, such as RpoS, RsmA andfIntraspecies or interspecies communication
networks
Red arrows
indicate
negative
regulation

The Las, Rhl and Qsc quorum-sensing systems in Pseudomonas aeruginosa:
hierarchies and integration into cellular control circuits. For each circuit in the cell theinteractions between the different QS systems are indicated by arrows. Black arrows indicate
positive regulation and red arrows indicate negative regulation. Signals from the environment,
the intracellular metabolic status of the cell and other regulators, such as RpoS, RsmA andf

AHL diffuses in

AHL diffuses out

Andree M. et.al. Nature Review Microbilogy, 2004

The quorum-sensing paradigm in Gram-negative bacteria
A. At low bacterial cell densities AHL molecules are synthesized andaccumulate. Depending on the length of the acyl chain, AHLs either diffuse
or are pumped out of the cell into the local environment, where the AHL
molecules are available for diffusion into, or uptake by, bacterial cells.

AHL molecules
LuxR Protein
Target geneAt high bacterial cell densities, the concentrationof AHL molecules has accumulated. When it
reaches to a threshold, the R protein will forms acomplex with its cognate AHL and this complex
can activates the transcription of target genes.
Rapid amplification of the AHL signal results or
facilitates the coordinate transcriptional
regulation of multiple genes.

E. coli protein-protein interaction networks
protein-protein

Jingchun Sun et.al. 2004, materials for the paper in Bioinformatics.

2. microRNA
In genetics, microRNAs (miRNA) are single-stranded RNA
molecules of about 21–23 nucleotides in length, which regulate
gene expression. miRNAs are encoded by genes that aretranscribed from DNA but not translated into protein (noncoding RNA); instead they are processed from primarytranscripts known as pri-miRNA to short stem-loop structures
called pre-miRNA and finally to functional miRNA. Mature
miRNA molecules are partially complementary to one or moremessenger RNA (mRNA) molecules, and their main function is
to downregulate gene expression.
Wikipedia, http://en.wikipedia.org/wiki

Total number of miRNAs: 8619
miRBase, Release 12.0: Sept 2008(http://microrna.sanger.ac.uk/sequences/)
Homo sapiens: 695
Rattus norvegicus: 286
Mus musculus: 488

miRNA Biogenesis
Transcription by Pol II
pri-miRNA(100s-1000s nts.)

Processing by Drosha
pre-miRNAs(~60 nts.)

NUCLEUS

NPC

Export by Exportin-5
CYTOPLASM

Processing by Dicer
ds miRNAs(~44 nts.)

Incorporation into RISC
by Argonaute
miRNA(~22 nts.)(repeat-associated siRNAs)

Sontheimer and Carthew (2005) Cell 122, 9-12
Andre,Verdel (2004) Science 672-676

Guide heterochromatin
effectors to DNA

Properties of miRNAs


21-23 nucleotides in length

microRNAs inhibit translation or reduce mRNA
leve:translational repression and mRNAdegradation

1000+ predicted miRNAs in humans regulating ~30%
of human genes

miRNA expression is tissue or developmental stage
specificmiRNA profiles are altered in human diseaseSome microRNAs function as tumor suppressors
while other microRNAs behave as oncogenes

3. Forward and reverse engineering
Reverse engineering(RE):Reverse engineering is the process of discovering thetechnological principles of a device, object or systemthrough analysis of its structure, function and operation.
It often involves taking something (e.g. a mechanicaldevice, electronic component, or software program)apart and analyzing its workings in detail, usually to try
to make a new device or program that does the samething without copying anything from the original.
Life Sciences: one of examples is as below:
From gene expression profiling going back into gene
regulatory networks and gene function modules.

Forward engineering(FE):Forward engineering is the process of moving from a highlevel abstraction and design to a low- level implementation.
In the most time, the forward engineering based on theinsights obtained from reverse engineering thensystematically improve the protocols of implementations.
Life Sciences: one of examples is as below:
From mRNAs, miRNSs, proteins going to gene regulatory
networks, PPI networks and metabolic pathways.

Previous works
 Constructing three different types of regulations
TF -> gene
microRNA -> gene
TF -> microRNACombinatory regulation networks

M. Levine, R. Tjian, (2003) Nature 424, 147.
Emerging evidence suggests that organismal complexity arises from progressively moreelaborate regulation of gene expression.



Hobert, (2004) Trends Biochem Sci. 29, 462.
TFs and miRNAs act in a largely combinatorial manner - that is, many different TFs or miRNAs
control one gene - and they act cooperatively on their targets - that is, there are several cisregulatory elements for a single TF or miRNA species in a target geneGeorge A. Calin , (2006) Nature Reviews, CANCER, 6
MiRNA-expression profiling of human tumours has identified signatures associated withdiagnosis, staging, progression, prognosis and response to treatment. Sometimes miRNAgenes might represent downstream targets of activated oncogenic pathways, or they target
protein-coding genes involved in cancer.

Previous works
 Topological analysis on the networks
 scale-free important vertexes and edges
 co-regulation relationship modules and functional annotation
Nicholas M. Luscombe, (2004) Nature, 431, 308
They present the dynamics of a biological network on a
genomic scale, by integrating transcriptional regulatory
information and gene-expression data for multiple conditions inyeast. They develop an approach for the statistical analysis of
network dynamics, called SANDY, combining well-known global
topological measures, local motifs and newly derived statistics.

Our methodology
Homo sapiens related
raw materials

D

B

F

G

Regulatory pairs
extracting

E

C

A

G

D
E

B
target CA
Filtering withmicroarray datasets

F
G

Model based
Topologicalanalysistarget C

Data integration
A

F

Reverse engineering

Forward engineering

Construct seq-match
based network

Database selection

Microarray datasets of microRNAs and genes
 Datas source
 NCI60 2007. The NCI-60, a panel of 60 diverse human cancer celllines used by the Developmental Therapeutics Program of the U.S.
NCI60 dataset contain tissue specific gene expression data for over
300 miRNAs, 18457 genes from nine cancers with 59 sub-phenotype
samples.. (http://discover.nci.nih.gov/cellminer/) Lu et al. MicroRNA expression profiles classify human cancers.
Nature. 2005 Jun 9;435:745-6
 Liu, T.et al., Detection of a microRNA signal in an in vivo expression
set of mRNAs, PLoS ONE, 2007, 2, 804.
 Pablo Landgraf, et al., A Mammalian microRNA Expression Atlas
Based on Small RNA Library Sequencing, Cell, 2007, Volume 129,
Issue 7, 1401-1414.
 GNF atlas 2007:high throughput gene expression atlas of mouse andhuman expression patterns across diverse tissue.(http://www.gnf.org)– miRGen– miRGen is an integrated database of positional relationships
between animal miRNAs and genomic annotation sets;
animal miRNA targets according to combinations of widelyused target prediction programs.
(http://www.diana.pcbi.upenn.edu/miRGen.html)– TRED– Transcriptional Regulatory Element Database. TRED
includes relatively complete genome wide promoter
annotation for human, mouse and rat; information ofavailability of transcription factor binding and regulation.
TRED can provide good training datasets for further
genome wide cis-regulatory element prediction, assist
detailed functional studies, and facilitate to decipher thegene regulatory networks.(http://rulai.cshl.edu/cgibin/TRED/tred.cgi?process=home)

– UCSC hg18 databases– The hg18 database contains location information of
miRNA on the human genome, and data report
page contains links to sequence and annotation data
for the genome assemblies featured in the UCSC
Genome Browser. (http: // hgdownload.cse. ucsc.
edu/downloads.html)– sanger miRBase– miRBase is the new home for microRNA data,containing 3 main sections: all published miRNA
sequences, genomic locations and associatedannotation; a newly developed database ofpredicted miRNA target genes; confidential service
assigning official names for novel miRNA genes
prior to publication of their discovery.
(http://microrna.sanger.ac.uk/)

Get regulatory information from data resources
TFs

genes

miRNA

genes

TFs

miRNAsCombinatory
regulation networks

Extract TF-gene pairs, miRNA-gene pairs and TF-miRNA pairs
from our selected data resources, then put them as input into ourreverse engineering model to get combinatory regulation
networks made by TF-gene pairs, miRNA-gene pairs and TFmiRNA pairs. This is our strategy of building miRNA regulatory
network based on forward and reverse engineeringGet regulatory information from data resources
TFs

genes

miRNA

genes

TFs

miRNAsExtract TF-gene pairs, miRNA-gene pairs and TF-miRNA pairs
from our selected data resources, then put them as input into ourreverse engineering model to get combinatory regulation
networks made by TF-gene pairs, miRNA-gene pairs and TFmiRNA pairs. This is our strategy of building miRNA regulatory
network based on forward and reverse engineering

TF -> gene
 Union from– UCSC: promoter defined as -1000 ~ +500 bps– TREDSo ur cet r an s cr ip t io n fa cto r ta r get

r ela t io n s h ip

u cs c p r ed ict ed

1 37

127 444

15 805

TRED

1 34

30 32

705 9

Union

2 14

16 354130 338

TF -> gene
 Union from
UCSC: promoter defined as -1000 ~ +500 bps
TRED

So ur cet r an s cr ip t io n fa cto r ta r get

r ela t io n s h ip

u cs c p r ed ict ed

1 37

15 805

127 444

TRED

1 34

30 32

705 9

Union

2 14

16 354130 338

microRNA -> target genes
 Union of– PicTar (http://pictar.mdc-berlin.de/)– TargetScan (http://www.targetscan.org/)– miRanda(http://cbio.mskcc.org/research/sander/data/miRNA2
003/miranda_new.html)
Pr ed iction algom icr o RNA t ar get
r elat io n sh ip
PicT ar 4 way
1 78
6 39 2
7 59 68
T ar get Scan
2 38
7 57 97 56 13m iRan d aXL
1 57
5 44 84 18 04
Unio n
2 76
1 02 55
1 18 40 8

TF->microRNA In order to obtain the information of how a TF regulate
miRNAs, we searched UCSC hg18 database again and got
total 421 human microRNAs, of which 123 located in genes.
(most in intron)‫‏‬.
 miRNA and gene Expression data are needed for calculate
the correlation of miRNA-gene pairs and were downloaded
from CellMiner (Blower, et al., 2007, Mol Cancer Ther ;Shankavaram, et al., 2007, Mol Cancer Ther.
(http://discover.nci.nih.gov/cellminer/home.do).
 After carefully selection we got two miRNA and gene
Expression datasets, 321 microRNA and 8388 gene areinclusive in these datasets.

TF->microRNA
Map miRNA onto genome we found 177 of
421 miRNA were located at ORF region,
most of them were located at intron
region. Through calculation of gene
expression correlation based on therelated miRNA and gene expression
data we found there exists
significant positive correlation forthe most of these kind of miRNA-genepairs. A logic conclusion can bereached for this results that thesemiRNA-gene pairs may share the same
regulatory region and mechanisms
directly or indirectly.The rightfigure show the statisticals i g n i f i ca n t o f ou r c o n c lu s i o n s.

TF->microRNA the rest 298 intergenic microRNAs can be divided into
157 clusters based on a selected distancecriteria(7.5kbp) on chromosome region, in which thelargest one includes 43 microRNAs

On chromosome region when the
distance of two miRNA is smallthen a certain criteria they can be
regarded as belonging to the same
From this figure we canthe rest 298 inter-geneticcluster.
microRNAs
be divided
seewhen distance canis biggerthan
7.5kb,
the numberof resulted
into 157 clusters based ona selecteddistanceclusters closes to a constant. We
criteria(7.5kbp) on chromosome
region,in whichselected 7.5kb
distance
as our thecriteria for miRNA clustering.
largest one includes 43 microRNAs

TF->microRNA



7.5kb

miRNAs in the same cluster havesignificant strong expression
level correlations. Both of Boxplots show the distribution ofPearson Correlation of miRNAs
the rest 298 inter-genetic microRNAs
canamong
be divided
expression level
157
The distanceleft Box-plot show
into 157 clusters based on clusters.
a selected
the results of miRNAs in thecriteria(7.5kbp) on chromosome
region, in which thesame cluster.

TF->microRNAlargest one includes 43 microRNAs

7.5kb

This picture show you a location
distribution of predicted TF
binding sites around TSS. CHIPon-chip data come from GDS1223
dataset of GEO database. Based onthethe298siteinter-genetic
TF restbinding
informationmicroRNAs can be divided
fromwe alsofoundintohg18
157dataset
clusters
based
on a selected distancethat in the region of –3K to 1K
criteria(7.5kbp)
on chromosome
region, in which thearoundTSS of the firstmiRNA ofeach
cluster
there
existslargest one includes 43 microRNAs
enriched TF binding sites.

TF->microRNA

-3kb

7.5kb

1kb

Linear approximation Hypothesis
Any gene’s expression level is mainly
controled by related TFs and miRNAs
and can be expressed as a union of
expression levels of TFs and miRNAs.

Forward and Reverse Engineering
Model based Data Integration

Multiple linear regression model-Reverse Engineering ModelE_g = Atf_g × Etf_g + Am_g × Em_g + interception + err
Em_g = Atf_g(m) × Etf_g(m) + interception' + err’
E_g: Expression level of a gene
Em_g: Expression level of a miRNA
Atf_g: A vector of TFs multi-action to gene g
Am_g: A vector of miRNAs multi-action to gene g
Atf_g(m): A vector of TFs multi-action to miRNA
Etf_g(m): Expression levels of miRNA related TFs
interception: a constanterr: random background from non-TFs factors

From gene and miRNA expression profilingto networks spanned by TFs, genes and miRNAs
Based on this Multiple linear regression modelwith A Restricted Conditions (Am_g <= 0) , wecan perform model based data integration andgenerate resulting regulatory network spanned
by TFs, genes and miRNAs through our forward
and reverse engineering methodology.

 Current research shown that many miRNA are associatedwith a number of tumor types, thus rendering their crucial
function in multiple disease processes such as oncogenesis(Debernardi et al, 2007, Leukemia) .

 Gene expression profiles from NCI60 dataset was used for
diciphering cancer related networks conbinated by TFs,
genes and miRNAs.
 Extracted datasets of TF-gene pairs, miRNA-gene paires
and TF-miRNA pairs were used for the input of reverse
engineering modelE_g = Atf_g × Etf_g + Am_g × Em_g + interception + err
Em_g = Atf_g(m) × Etf_g(m) + interception' + err’forwardNCI60
dataset……

outputReverseengineering
model

input

reverse

Reverse Engineering Model based
data integration

TFs -> genes
miRNAs -> genes
TFs -> miRNAs
Least squared
estimating (LSE)
method was used here
to select suitable
regulator-target pairs

Resulted microRNA-gene regulate network”NCI60” dataset related microRNA-gene regulate network
 The network has 3418 vertexes(genes, miRNAs)
159 microRNAs and 3259 genes
 The network contains 222 regulators
154 microRNAs, 68 TFs
 3295 regulated targets are found
58 microRNAs, 3201 genes
 The network includes 5136 regulate relationships
1625 microRNA – target, 3413 TF – target gene and
98 TF –target microRNA.

 In generated regulatory network 32.3% aremicroRNA mediated regulations, 67.7% are TF
mediated regulations; In this case, microRNAs initiated gene regulation
is not only a fine spinner to a normal TF mediatedregulation but alone a good supplementary to it; For cancer related biological processes, it looks
like miRNA may play some very crucial role in
the formation and development of tumors or
oncogenesis.

Global Network Spanned by Regulators

Network detail

TFs
miRNA
genes

Validation of generated network

Estimating FDR of Generated Network Shuffling microRNA dataset and re-modeling
network with the same threshold α=0.05

type
shuffled network original network
microRNA- > target gene 477.8(+- 29.2)
1625
TF- > target gene382.3(+- 23.9)
3413
TF- > target microRNA
21(+ - 5.2)
98
overall
881.1(+- 46.2)
5136

FDR(%)
29.4(+-1.8)
11.2(+ - 0.7)
21.5(+ - 5.3)
17.2(+ - 0.9)

We shuffled our miRNA-gene pairs and re-calculate our reverse
engineering model to get a random network, and repeat thissteps 100 times to estimate FDR of the resulted network

Reverse engineering Model based Gene
Expression level Prediction--From regulators to infer Gene Expression level
 E_g = Atf_g × Etf_g + Am_g × Em_g + interception + err
 Em_g = Atf_g(m) × Etf_g(m) + interception' + err’
If we know expression levels of each regulators, then we canbased on this reverse engineering model calculate or predict
expression levels of target genes or miRNAs. Through thecomparison of gene expression level between predicted andexperimental data we can estimate the ability of our model.

Reverse engineering Model based Gene
Expression level Prediction--From regulators to infer Gene Expression level

We calculated Pearson correlation coefficients among predicted andexperimental expression level data. Results shown there exist highcorrelations among predicted results and experimental results.

Topological analysisof the network

Topological Structure of Generated Network
Network is scale
free, and R-squared
value is 0.8506, theslope is -1.259.

The diameter of

Global Network Spanned by Regulators
the network is

14 ( for social
network the
diameter is 6 )Method see: MEJ Newman and M Girvan: Finding and evaluating
community structure in networks. Physical Review E, 2004.

modularity of
Global Network Spanned byTheRegulators

generated score is 0.67,
denoting the highly
modularized structureof the original
regulatory network.

Method see: MEJ Newman and M Girvan: Finding and evaluating
community structure in networks. Physical Review E, 2004.

network can
Global Network SpannedWholeby regulatory
Regulators

Extracted
Extracted
sub-network
sub-network

be decomposed into 25 distinct
modules using a community
identification algorithm, andeach module can be assigned
several biological functions or
pathways via enrichment
analysis of genes involved in.

Method see: J. Reichardt and S. Bornholdt: Statistical Mechanics ofCommunity Detection, Phys. Rev. E, 2006.

Box-plots show the relationship between
Box-plots
in-degree and out-degreein-degree
out-degree
Y:
Y:numbernumberof
ofregulated
regulatedtargets
targets(out-degree(out-degree))X:
X:numbernumberof
ofdifferent
differentregulators
regulators(in-degree(in-degree))A digraph with vertices labeled (indegree, outdegree)
http://en.wikipedia.org/wiki/Out-degreeY:
Y:numbernumberof
ofregulated
regulatedtargets
targets(out-degree(out-degree))X:
X:numbernumberof
ofdifferent
differentregulators
regulators(in-degree(in-degree))

It looks quite significant thatout-degree and in-degree arepositive correlated and theimportant regulators (withmore targets ) are more
likely to be regulated byother regulator.

Here is an example.
Regulator MYC has mosttargets in our generated
network and it is also
found to be regulated bymore regulators (by 6regulators) to form a
crucial target-hub.We knowthat MYC is involved inmany ontogenesis processes.

Importance estimation of vertexes andedges in network
 Some vertexes in the network were more important thanothers because they were in special topological position;
 More important regulators are more likely to regulate
others or be regulated by more regulators according toout and in degrees, betweenness and page rank scores.
Method for calculation of betweenness and page rank score see: Ulrik
Brandes et al, Journal of Mathematical Sociology, 2001. Sergey Brinand Lawrence Page, article: “The Anatomy of a Large-Scale
Hypertextual Web Search Engine”.

TF

out.degree

microRNA

out.degree

namebetweenness

namepage.rank.score

MYC

301

hsa-mir-106b

44

MYC

46215.8667

MYC

93.8855139

JUN

174

hsa-mir-19b

40

ETS1

32284.5

JUN

52.688434

YY1

149

hsa-mir-25

40

JUN

31763.1667

YY1

47.5034229

TFAP2A

122

hsa-mir-200b

38

MAX

26029.9667

TFAP2A

36.6842617

NFE2L1112

hsa-mir-96

36

hsa-let-7c

25033.0167

ELK1

34.965482

E2F2

111

hsa-mir-23a

35

ZNF23824021.2333

NFE2L133.9376032

CUTL1

109

hsa-mir-141

34

TFAP2C

22370.45

CUTL1

33.7039168

ELK1

109

hsa-mir-30d

34

TP53

19551

E2F2

32.6829685

NR3C1

101

hsa-mir-23b

32

RUNX1

15570.7667

NR3C1

31.449525

PPARG

91

hsa-mir-128b

31

NFE2L115041.3333

PPARG

28.474886

STAT1

90

hsa-mir-106a

29

NR2F2

14846.5667

ETS1

27.8171399

ETS1

85

hsa-mir-138

29

PPARG

8030.25STAT1

26.7622651

XBP1

85

hsa-mir-194

27

hsa-mir-106b

7880.4NFIA

26.4860635

NFIA

83

hsa-mir-130a

25

hsa-mir-220

7578.4

XBP1

26.267848

NFYB

82

hsa-mir-20

25

TFAP2A

7432.13333

NFYB

25.3015407

RUNX1

80

hsa-mir-27b

25

ARID5B

6811.26667

RUNX1

24.8231955

E2F4

74

hsa-mir-19a

24

CUTL1

6704.46667

E2F4

22.329289

POU3F270

hsa-mir-20b

24

FOSL1

5616.56667

POU3F2

21.9473661

 MYC is the TF with most targets, highest betweenness andhighest page rank score in the network; Based on the topological properties of MYC in the network,
MYC could be ranked the most important regulator ingenerated network;
 MYC has 301 targets included 291 genes and 10
microRNAs.
 To validate this results those target genes were mapped andcompared to CHIP-chip dataset of MYC in GDS1223(GEO
database). We found that promoter regions of most target
genes were likely to have binding site of MYC.

CHIP-chip validation for MYC
A heat-map visualizing thebinding status between MYC andpromoters of our predicted MYC
targets in five replicated CHIPchip experiments. Rows indicatedtargets and columns indicatedexperiments. Red color meant
there was a significant binding
between MYC and the promoter ofthe corresponding target (row) atcorresponding experiment(column). Green color meant there
was no significant binding
evidence between them.

CHIP-chip validation for MYC
Binding
Prediction binding

targetnon- target

Non-binding
nonbindin
60
231
386
7711

RTDR = 87%
RTDR: Relative True Discovery Rate

The MYC centered regulatory network

The red vertexes were
The MYC centered regulatory
genesnetwork
validated by

CHIP-chip dataset, thegray vertexes were
genes not validated andthe white vertexes were
genes not involved in
the CHIP-chip dataset

Power of Prediction
 The most of predicted miRNAs in MYC
centered sub-network can be found withstrong literature support(9 of 10), andquite a lot of genes which have been
experimentally conformed to be MYC
target genes are in our MYC centeredsub-network(over 60 of 291).

TF

out.degree

microRNAMYC

301

hsa-mir-106b
A

JUN

174

hsa-mir-19b

out.degree

namebetweenness

namepage.rank.score

44

MYC

46215.8667

MYC

93.8855139

40

ETS1

32284.5

JUN

52.688434

YY1

149

hsa-mir-25

40

JUN

31763.1667

YY1

47.5034229

TFAP2A

122

hsa-mir-200b

38

MAX

26029.9667

TFAP2A

36.6842617

NFE2L1112

hsa-mir-96

36

hsa-let-7c

25033.0167

ELK1

34.965482

E2F2

111

hsa-mir-23a

35

ZNF23824021.2333

NFE2L133.9376032

CUTL1

109

hsa-mir-141

34

TFAP2C

22370.45

CUTL1

33.7039168

ELK1

109

hsa-mir-30d

34

TP53

19551

E2F2

32.6829685

NR3C1

101

hsa-mir-23b

32

RUNX1

15570.7667

NR3C1

31.449525

PPARG

91

hsa-mir-128b

31

NFE2L115041.3333

PPARG

28.474886

STAT1

90

hsa-mir-106a

29

NR2F2

14846.5667

ETS1

27.8171399

ETS1

85

hsa-mir-138

29

PPARG

8030.25STAT1

26.7622651

XBP1

85

hsa-mir-194

27

hsa-mir-106b

7880.4NFIA

26.4860635

NFIA

83

hsa-mir-130a

25

hsa-mir-220

7578.4

XBP1

26.267848

NFYB

82

hsa-mir-20

25

TFAP2A

7432.13333

NFYB

25.3015407

RUNX1

80

hsa-mir-27b

25

ARID5B

6811.26667

RUNX1

24.8231955

E2F4

74

hsa-mir-19a

24

CUTL1

6704.46667

E2F4

22.329289

POU3F270

hsa-mir-20b

24

FOSL1

5616.56667

POU3F2

21.9473661

has-miR-106b centered regulatory network
 MicroRNA hsa-miR-106b has the largest number(44) of target genes in the network; hsa-miR-106b is one of ten miRNAs regulated byMYC;
 38 of 44 target genes were interrogated in GSE6838,
a microarray dataset(GEO database) of microRNA
over-expression experiment.

has-miR-106b centered regulatory network
Validated bytransfection experiments
Not conformed bytransfection experiments
No transfection
experiment information

GSE6838 dataset (GEO)
over-expression experiment

regulator

targetEstimate

Pr...t..

betweenness

hsa-let-7c

MYC26871.35

MAXJUN−0.81614678 0.309101075 −2.6403880 0.010961875−0.47212555 0.314958336 −1.49900952 0.139694978

25511.66667

MYC

MAX

0.073810859

0.041428227

1.781656246

0.080535127

21810.96667

ETS1

TP53

0.1750833

0.074262212

2.357636476

0.02191082

17534

JUN

ETS1

0.234747154

0.111163342

2.111731712

0.039728628

16622

NR2F2

NFE2L1

0.091388685

0.031367978

2.913438865

0.005098112

15083.33333

RUNX1

ZNF2380.408633049

0.102460766

3.988190475

0.000213047

14002.66667

TP53

TFAP2C0.52776269

0.257530514

2.049321

0.045212408

12866.83333

NFE2L1hsa-let-7c

0.560361582

0.238233351

2.352154225

0.02227222

12824

Std..Error

t.valuemiRNA with the highest betweenness
as a regulator of MYC
 Experiment shown that has-let-7c regulates MYC;
(Yatrik M. Shah et al., Molecular and Cellular Biology, 2007)

 Our predicted model is consistent with experimentresults, has-let-7c regulates MYC in the network.
Besides, has-let-7c has the highest betweenness ingenerated network, which means has-let-7c will
possibly have more functional connections with othergenes in the network than that of those genes withlower betweenness;
 Topological structure properties may also help todecipher the importance of biological functions.

Sub-network centered by has-let-7c

MYC
MYC
Sub-network centeredby has-let-7c
regulated
regulatedbyby has-lethas-let7c
7c

Significant co-regulating regulator pairs
If two regulators (TFs, miRNAs)
significantly share more targets in thegenerated network, it can be considered a
co-regulator pair.
 17 TF-TF pairs
 21 microRNA-microRNA pairs
 7 TF-microRNA pairsSignificant co-regulatory pairs
It was found in our co-regulator list:
 MYC-MAX: a well known transcriptional complex.
 JUN, JUNB, JUND and FOSL1 forming AP-1
transcriptional complex.
 NFKB1and RELA were another transcriptional complex.
 microRNA within same family tended to regulate sametargets,such as let-7 family.
 Most co-regulating pairs were labeled in same sub-network
cluster divided previously.
Kang Tu, et.al., Nucleic Acid Research revised.

Applying to concrete diseases or drug
mechanisms
 Integrating data from concrete disease or somechemical perturbations related gene expression
profiling For example small molecules/drugs induced gene
expression profiling as control.

Lamb, ea al., 2006. The Connectivity Map: Using Gene-Expression Signatures toConnect Small Molecules, Genes, and Disease. Science, V. 313, 1929-1935.

Mitochondrial physiology and gene expression across 2,490 chemical perturbations. The calcein assay(1)measures cell viability and filters out overtly toxic compounds. The MTT assay (2) measures cellulardehydrogenase activity, which is inhibited by the complex I inhibitor rotenone. The JC-1 assay (3) measuresthe mitochondrial membrane potential (DCm) and drops acutely after the addition of the mitochondrial
uncoupler carbonyl cyanide m-chlorophenylhydrazone (CCCP). A luciferase-based assay measures ATP (4),which is reduced by staurosporine. CM-H2DCFDA is a fluorescent probe of cellular ROS (5), which can be
stimulated by the addition of H2O2. The expression of both nuOXPHOS and mtOXPHOS transcripts ismeasured by a multiplex PCR technique, GE-HTS (6). Each column of the heat map represents one sample
replicate; expression levels for each gene are row-normalized. Treatment with PGC-1a, an inducer of
OXPHOS gene expression, is used as a positive control. All assays were performed in biological duplicate in
384-well format after 48 h of treatment in differentiated murine C2C12 myotubes. Data from 2,490 distinct
compounds are incorporated into the screening compendium. Bridget, et al., 2008, Large-scale chemicalditi
f it h d i l fti
NAT BIOTECH V 26 N 3

Small Molecule (drugs) Regulation

Docking

3

Targetproteins

Small molecules

Filtering
2

InducedmutationsBiological effects related genes or GOMs

4

Small molecule regulated
signal transduction networks

Gene expression profiling

1

Procedures for the gene expression profilingrelated small molecule alignment

Yun Li, et.al., 2008 Nucleic Acid Research.

E_g = Atf_g × Etf_g + Am_g × Em_g + interception + err
Em_g = Atf_g(m) × Etf_g(m) + interception' + err’forwardSmallmolecules
inducedexpression
profiling

outputReverseengineering
model

input

TFs -> genes
miRNAs -> genes
TFs -> miRNAs

reverse

Reverse Engineering Model based data integration

Summary Combinations of forward and reverse engineering together
with suitable mathematical model can be used to constructregulatory network which consists of TFs,miRNAs andgenes.
 We present a new methodology for constructing regulatory
networks spanned by TFs,miRNAs and other target genes aswell; Multivariate Linear Model with A Restricted Conditions
could be a powerful mathematical model to integrate gene
expression profiling data and regulator pairs data; Topological analysis of network structures can help
to reveal regulator’s importance and relevant functions.
 NCI60 is a useful dataset which can be used for
deciphering cancer related miRNA regulatory
mechanisms. Cancer type dependent genes andmicroRNAs can be identified using NCI60 datasets.
 Based on this methodology other carefully selected gene
expression profiling datasets can be also used to generate
special regulatory networks.

Acknowledgements
Dr. Kang Tu, Dr. Xie Lu, Prof. Lei Liu

Thank you for yourAttention!