Perturbing The Interactome: Multi-Omics And Personalized Methods For Network Medicine

PERTURBING THE INTERACTOME
MULTI-OMICS AND PERSONALIZED METHODS FOR NETWORK
MEDICINE
Marc Santolini

Center for Complex Network Research (CCNR)

Asthma
Parkinson’s
Leukemia
MS
Hypertension
Rheumatoidarthritis
Crohn’s disease
Type 2 diabetes
Glioblastoma
Ulcerative colitis
Heart failure
Network Clustering Means Explainable Biology
AIF1
ZBTB12
NFKBIZ
MERTK
HHEX
CFB
Diseases As Local Neighborhoods

DIAM
DISEASE MODULES VS COMMUNITIES

−1.00.00.51.0
Fold-Change
BXD−14/TyJ
BXA16/PgnJ
DBA/2J
BXA24/PgnJ
BXD32/TyJ
BXD−32/TyJ
BXHB2
KK/HlJ
AXB19/PgnJ
CXB−3/ByJ
AXB−20/PgnJ
BXD75
BUB/BnJ
LG/J
NOD/LtJ
BXD−11/TyJ
FVB/NJ
PL/J
CXB−12/HiAJ
BXA−4/PgnJ
BXD66
BXD62
SJL/J
CXBH
BXD50
BXH−19/TyJ
BXD87
RIIIS/J
SEA/GnJ
129X1/SvJ
BXD56
SM/J
BXH−9/TyJ
BXD48
BALB/cByJ
BXA−2/PgnJ
C3H/HeJ
CBA/J
BXD45
BXD−38/TyJ
CXB−7/ByJ
AXB−6/PgnJ
BXD49
BALB/cJ
BXD73
BXD40/TyJ
BXD−40/TyJ
BXD43
AKR/J
CXB−6/ByJ
BXA14/PgnJ
C57BLKS/J
BXD64
BXD61
AXB10/PgnJ
BXA−8/PgnJ
BXD21/TyJ
BXA−1/PgnJ
NZB/BlNJ
BXD68
NON/LtJ
SWR/J
BXD84
BXD74
BXD71
BXD79
BXD−5/TyJ
BXA−7/PgnJ
BXA−11/PgnJ
BXD55
BXD−12/TyJ
BXD85
AXB18
BXD44
CE/J
AXB8/PgnJ
NZW/LacJ
BXD−24/TyJ
BXD70
BXD39/TyJ
LP/J
CXB−11/HiAJ
BXH−6/TyJ
CXB−13/HiAJ
1.01.31.6
Fold-Change(log2)
Hes1
Heart mass
Hes1 downregulation
Mild hypertrophy
Hes1 upregulation
Strong hypertrophy
I. Topology vs dynamics
in perturbation
spreading
II. Network
perturbation by
miRNAs
III. Personalized
response to a
perturbation
OVERVIEW

PART I
TOPOLOGY VS DYNAMICS IN PERTURBATION
SPREADING
Marc Santolini, Albert-László Barabási, “Predicting Perturbation Patterns From The Topology Of
Biological Networks”

EDGE TYPES
undirected
PPI
directed
Kinase/substrate
Gene regulation
Metabolic
miRNA
A
B
signed
activation / inhibition
B
A
B
A
weighted
kinetic parameters
˙xi = f(xi, xj, ✓ij)
BA BA

Perturbation
(variant, drug
target)

Perturbation
(variant, drug
target)
Perturbation
pattern in the
network

full network
dynamical system
topology
backbone

‣How much network complexity do we
need to predict perturbation patterns?
full network
dynamical system
topology
backbone

‣87 dynamical systems of biological processes of size > 10

RETRIEVING BIOCHEMICAL NETWORKS
Weighted signed
directed network
System of differential equations
b
c
a
... ...
...
...
...
8
>><
>>:
˙xa = k1xc
˙xb = k2
xc
k3+xa
k4xb
˙xc = k5xb k6xc
. . .
Inﬂuence networkJacobian matrix
J =
2
6
6
6
4
0 0 k1 . . .
k2
x⇤
c
(k3+x⇤
a)2 k4
k2
k3+x⇤
a
. . .
0 k5 k6 . . .
...
...
...
...
3
7
7
7
5
how a change in a node
concentration directly
inﬂuences another node
at steady state
Biochemical network

“Onion-peeling” strategy: how much each layer of complexity
adds to the accuracy of perturbation patterns

Biochemical Topological
Weight (kinetic parameters)
+
+
+
+
+
+
+
-
+
+
-
-
+
-
-
-
Topology (interaction)
Direction (irreversibility)
Sign (activation/inhibition)
ONION-PEELING
J sign(J) |sign(J)| (|sign(J)| + |sign(JT
)|)
2

ONION-PEELING
PPI
+
+
+
+
+
+
+
-
+
+
-
-
+
-
-
-
)|)
2

ONION-PEELING
PPI
Integration with
kinome,
metabolome,
miRnome, gene
regulatory network
+
+
+
+
+
+
+
-
+
+
-
-
+
-
-
-
)|)
2

ONION-PEELING
Literature,
genetic
interactions
Integration with
kinome,
metabolome,
miRnome, gene
regulatory network
+
+
+
+
+
+
+
-
+
+
-
-
+
-
-
-
)|)
2
PPI

ONION-PEELING
Bottleneck
Literature,
genetic
interactions
Integration with
kinome,
metabolome,
miRnome, gene
regulatory network
+
+
+
+
+
+
+
-
+
+
-
-
+
-
-
-
)|)
2
PPI

PERTURBATION PATTERNS
Perturbation spread computed from the Jacobian matrix
(Barzel, Barabasi, Nat Biotech 2013)
S = (1 J) 1
D(
1
(1 J) 1
)
Perturbation patterns
(long distance correlations)
Jacobian
(direct inﬂuence)
Sij =
dxi/xi
dxj/xj
Jij =
@xi/xi
@xj/xj
‣ Similar to PRINCE algorithm for unweighted network
(Vanunu et al, PLoS Comp Biol 2009)

Biochemical
Perturbed node
Effect
Network
Perturbed nodePerturbed node
Effect
Biochemical
Network
ACCURACIES ACROSS 87 MODELS
Accuracy of
perturbation patterns

(dashed line: Z=2 random
expectation)
Topology
Direction
Sign
Kinetics
+
+
+
+
+
+
+
-
+
+
-
-
+
-
-
-
Accuracyofperturbationpatterns
Biochemical
Diffusion(d+s)
Distance(d)
Firstneighbors(d+s)
Influencestrengthrecovery
0.00.20.40.60.81.0
Accuracy of
Biochemical
Perturbed node
Effect
Network
Effect
Biochemical
Network

‣ 65% recovery rate without kinetics
(80% when just looking at sign recovery)
(dashed line: Z=2 random
expectation)
Topology
Direction
Sign
Kinetics
+
+
+
+
+
+
+
-
+
+
-
-
+
-
-
-
Biochemical
Diffusion(d+s)
Distance(d)
Firstneighbors(d+s)
0.00.20.40.60.81.0
Accuracy of
Biochemical
Perturbed node
Effect
Network
Effect
Biochemical
Network
kinetics

OTHER FEATURES
Best models are modular, sparse and have fewer hubs
Number of structural holes
Edge density
Mean degree
Mean Jacobian value
Mean eigenvector centrality
Number of reversible equations
Model size
Mean betweenness centrality
Number of strong connected components
Correlation with network model accuracy
−1.0 −0.5 0.0 0.5 1.0
●
●
●
●
●
●
●
●
●

MODULARITY BUFFERS PERTURBATIONS
INSIGHTS | PERSPECTIVES
:A.KITTERMAN/SCIENCE
By Marta Sales-Pardo
I
n the 1970s, ecologists began to specu-
late that modular systems—which are
organized into blocks or modules—can
better contain perturbations and are
therefore more resilient against exter-
nal damage (1). This simple concept can
be applied to any networked system, be it
an ecosystem, cellular metabolism, traffic
flows, human disease contagion, a power
grid, or an economy (2, 3). However, experi-
mental evidence has been lacking (4). On
page 199 of this issue, Gilarranz et al. (5)
provide empirical evidence showing that
modular networked systems do indeed have
an advantage over nonmodular systems
when faced with external perturbations.
The authors designed an elegant meta-
population experiment with the arthro-
pod Folsomia candida. In the experiment,
habitat patches (nodes) were connected in
a modular fashion; specimens could freely
move between patches. Initially, the au-
thors inoculated all patches with the same
number of specimens. Once the whole sys-
tem had reached a stable population, they
continuously removed specimens from one
of the nodes for a sustained period of time.
The results show that modular systems can
contain the spread of such a perturbation
more effectively than a nonmodular system
would, so that only the populations of nodes
within the same module as the perturbed
node is affected (see the figure).
Gilarranz et al. also show that the capac-
ity to contain perturbations comes with a
cost. The more modular the system, the
larger the overall population in the presence
of even strong perturbations, but that is not
the case in the absence of a perturbation. In
the latter case, the overall population levels
are higher in nonmodular systems. These
results not only align with theoretical ex-
pectations, they also help explain the large-
scale organization of complex systems.
To further put this result in historical con-
text, we should cast our minds back more
than 10 years. At that time, the definition
of module in a networked system was still a
loose concept, especially for large systems (6).
Based on commonalities in the systems-level
organization of large networked systems, net-
work scientists proposed to define a module
as a set of nodes that are densely connected
among themselves but loosely connected
to other parts of the network (7, 8). This is
the definition that Gilarranz et al. use. The
modularity of a network then quantifies how
well-defined these densely connected groups
of nodes are within the network.
Using this definition of modules, scien-
tists soon found that the vast majority of
large-scale networked systems in any con-
text—including social, biological, ecologi-
cal, and engineering systems—have a strong
modular component (9, 10). The question
arose why this was the case. In engineered
systems, modularity is arguably a useful
design principle because modular systems
are easy to fix and update (11). However, the
justification for the modularity of natural
networked systems mostly hinged on the
aforementioned theoretical stability consid-
erations. The results reported by Gilarranz
et al. show that the need to contain pertur-
bations can plausibly explain the modular-
ity of at least some natural systems.
NETWORKS
The importance of being modular
An experiment proves the value of modularity in complex systems under perturbation
Department of Chemical Engineering, Universitat Rovira i
Virgili, 43007 Tarragona, Spain. Email: marta.sales@urv.cat
System state
RealityRandomModular
Perturbation starts Perturbation spreads
Contained
in a module
Perturbed
node
Node
Module
Spread?
Spread?
Uncontained
spread
Interconnected
network
How to contain perturbations
In modular systems,perturbations can be contained in one module,whereas they spread throughout randomly connected systems.Real systems could be
represented as a combination of the two types,making it difficult to predict the effects of perturbations.
onSeptember14,2017http://science.sciencemag.org/Downloadedfrom
128 14 JULY 2017 • VOL 357 ISSUE 6347 sciencemag.org SCIENCE
GRAPHIC:A.KITTERMAN/SCIENCE
be applied to any networked system, be it
an ecosystem, cellular metabolism, traffic
flows, human disease contagion, a power
grid, or an economy (2, 3). However, experi-
mental evidence has been lacking (4). On
page 199 of this issue, Gilarranz et al. (5)
provide empirical evidence showing that
modular networked systems do indeed have
an advantage over nonmodular systems
when faced with external perturbations.
The authors designed an elegant meta-
population experiment with the arthro-
pod Folsomia candida. In the experiment,
habitat patches (nodes) were connected in
a modular fashion; specimens could freely
move between patches. Initially, the au-
would, so that only the populations of nodes
within the same module as the perturbed
node is affected (see the figure).
Gilarranz et al. also show that the capac-
ity to contain perturbations comes with a
cost. The more modular the system, the
larger the overall population in the presence
of even strong perturbations, but that is not
the case in the absence of a perturbation. In
the latter case, the overall population levels
are higher in nonmodular systems. These
results not only align with theoretical ex-
pectations, they also help explain the large-
scale organization of complex systems.
To further put this result in historical con-
text, we should cast our minds back more
than 10 years. At that time, the definition
of module in a networked system was still a
loose concept, especially for large systems (6).
well-defined these densely connected groups
of nodes are within the network.
Using this definition of modules, scien-
tists soon found that the vast majority of
large-scale networked systems in any con-
text—including social, biological, ecologi-
cal, and engineering systems—have a strong
modular component (9, 10). The question
arose why this was the case. In engineered
systems, modularity is arguably a useful
design principle because modular systems
are easy to fix and update (11). However, the
justification for the modularity of natural
networked systems mostly hinged on the
aforementioned theoretical stability consid-
erations. The results reported by Gilarranz
et al. show that the need to contain pertur-
bations can plausibly explain the modular-
ity of at least some natural systems.
Department of Chemical Engineering, Universitat Rovira i
Virgili, 43007 Tarragona, Spain. Email: marta.sales@urv.cat
System state
RealityRandomModular
Perturbation starts Perturbation spreads
Contained
in a module
Perturbed
node
Node
Module
Spread?
Spread?
Uncontained
spread
Unperturbed nodes in modular systems Unperturbed nodes in randomly connected systems Perturbed nodes
Interconnected
network
How to contain perturbations
In modular systems,perturbations can be contained in one module,whereas they spread throughout randomly connected systems.Real systems could be
represented as a combination of the two types,making it difficult to predict the effects of perturbations.
Published by AAAS
onSeptember14,2017http://science.sciencemag.org/Downloadedfrom
Science, 2017

CHEMOTAXIS
Molecular Biology of the Cell
Vol. 4, 469-482, May 1993
Computer Simulation of the Phosphorylation Cascade
Controlling Bacterial Chemotaxis
Dennis Bray,* Robert B. Bourret,t# and Melvin I. Simont
*Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, United Kingdom; and
tDivision of Biology, California Institute of Technology, Pasadena, California 91125
Submitted January 22, 1993; Accepted March 5, 1993
We have developed a computer program that simulates the intracellular reactions mediating
the rapid (nonadaptive) chemotactic response of Escherichia coli bacteria to the attractant
aspartate and the repellent Ni2+ ions. The model is built from modular units representing
the molecular components involved, which are each assigned a known value of intracellular
concentration and enzymatic rate constant wherever possible. The components are linked
into a network of coupled biochemical reactions based on a compilation of widely accepted
mechanisms but incorporating several novel features. The computer motor shows the same
pattern of runs, tumbles and pauses seen in actual bacteria and responds in the same way
as living bacteria to sudden changes in concentration of aspartate or Ni2+. The simulated
network accurately reproduces the phenotype of more than 30 mutants in which com-
ponents of the chemotactic pathway are deleted and/or expressed in excess amounts and
shows a rapidity of response to a step change in aspartate concentration similar to living
bacteria. Discrepancies between the simulation and real bacteria in the phenotype of certain
mutants and in the gain of the chemotactic response to aspartate suggest the existence of
additional as yet unidentified interactions in the in vivo signal processing pathway.
INTRODUCTION
Coupled protein phosphorylations are a universal
mechanism of intracellular signaling and the basis of
many complex nets of reactions in eucaryotic cells. Such
networks are poorly understood, despite their impor-
tance, because of the large number of components in-
volved, and the multiple links that exist between them.
Not only are quantitative data on the individual reac-
tions lacking, but we also lack a vehicle by which we
can comprehend the implications of such data and verify
the overall performance of the entire network. Without
making many detailed calculations, it will be difficult
to predict how the network performs under a particular
set of starting conditions, whether it will ever show os-
cillatory or chaotic behavior, and how the performance
of the network will be affected by genetic modifications.
The rational solution to this problem is to employ a
computer simulation that can be used to catalogue bio-
chemical data, test current hypotheses of mechanism,
and make predictions of performance under a virtually
f Present address: Department of Microbiology and Immunology,
University of North Carolina, Chapel Hill, NC 27599.
limitless variety of conditions. However, there have
been very few attempts to develop quantitative models
of this sort for cell signaling pathways involving coupled
phosphorylation reactions. Early steps in this direction
include models of the spatial propagation of oscillations
in Ca2+ concentration in a variety of cells (reviewed by
Dupont and Goldbeter, 1992) and the initial response
to light of vertebrate photoreceptors (Lamb and Pugh,
1992). For the vast majority of cell signaling pathways,
there is insufficient information to construct a mean-
ingful simulation. Without knowing most of the relevant
intracellular concentrations and enzymatic rate con-
stants in a pathway, computer simulations can only be
phenomenological representations having little analyt-
ical power or predictive force at the molecular level.
One of the few signaling pathways for which there
is presently sufficient information to attempt a detailed
computer simulation is that underlying the chemotactic
response in coliform bacteria. This entails a small and
defined set of intracellular proteins which, through
coupled phosphorylation and methylation reactions,
enable the bacterium to respond in an informed, if not
intelligent, way to its chemical environment. All of the
intracellular proteins involved in this response have
©) 1993 by The American Society for Cell Biology 469
Table 5. Comparison of simulated and observed chemotactic behavior of mutant bacteria
Bias
Genotype Simulated Observed Interpretation in terms of bct 1.1 model References
sm sm Most Yp made by TW-stimulated A
tm tm More p flows to Y in absence of B
sm sm Most Yp made by TW-stimulated Y
sm sm No Yp
sm sm No Yp
tm tm Yp increases in absence of Z
wt wt Unstimulated A makes enough Yp in absence of Z
tm ? More p flows to Y in absence of B and Z
tm ? More p flows to Y in absence of B and Z
wt wt Unstimulated A makes enough Yp in absence of Z
wt wt Unstim"lated A makes enough Yp in absence of Z
sm sm No Yp, even in absence of Z
sm sm No Yp
sm sm No Yp
smc sm T overproduction sequesters A and TA complex
sm sm B removes phosphate from A, thus reducing Yp
sm sm W overproduction sequesters A in AW complex
tmd
tm
sm
wt'
tm
T2+A2+
y2+z2+
B2+y2+
"Gutted" mutantsa
(gutted)
A+ (gutted)
Y+ (gutted)
y2+ (gutted)
Y3+ (gutted)
W+Y+ (gutted)
T+W+Y+ (gutted)
A+Y+ (gutted)
A2`Y+ (gutted)
W+A+Y+ (gutted)
T+A+Y+ (gutted)
T+W+A+Y+ (gutted)
Mixed overproduction/
null mutants
'2+Z-
B2+Z-
W2+Z
A2+Z-
Y2+z-
Y2+B-Z-
T2+W-Z-
w2+B-
Z2+B-
Y2+T-
sm A overproduction leads to more Yp
tm Y overproduction leads to more Yp
sm Z stimulates loss of Yp
wt T & W form a complex to restore balance
sm W overproduction doesn't turn off enough A
tm ? A overproduction leads to more Yp
wt' wt Z overproduction balances Y overproduction
sm' sm p flows preferentially through B rather than Y
sm sm No Yp
sm ? No Yp
sm' sm No YP when Y moderately overproduced
tm tm Very high levels of CheY cause CW rotation
tm sm Unstimulated A makes too much Yp in absence of Z
tm ? A overproduction leads to more Yp
tm tm Stimulated A makes Yp
tm wt T overproduction doesn't turn off enough A
tm sm B overproduction doesn't remove enough p from A
tm sm W overproduction doesn't turn off enough A
tm tm A overproduction and absence of Z lead to more Yp
tm ? Y o,verproduction and absence of Z lead to more Yp
tm tm More p flows to Y in absence of B and Z
smc sm T overproduction turns off unstimulated A
sm sm W overproduction sequesters A, converting B- to sm
sm sm Z overproduction reduces Yp, converting B- to sm
tm tm Unstimulated A makes Yp when Y overproduced
Liu and Parkinson (1989)
Parkinson (1978)
Parkinson (1978)
Parkinson (1978)
Parkinson (1978)
Parkinson (1978)
Wolfe et al. (1987)
Stewart et al. (1988)
Liu and Parkinson (1989),
Sanders et al. (1989)
Clegg and Koshland (1984)
Kuo and Koshland (1987)
This work
This work
Wolfe et al. (1988)
Wolfe et al. (1988)
Wolfe et al. (1988)
Barak and Eisenbach (1992)
Conley et al. (1989)
This work
This work
Wolfe et al. (1987)
Kuo and Koshland (1987)
Clegg and Koshland (1984)
a "y'+ (gutted)" refers to a strain in which CheY is expressed at wild-type concentration in the absence of Tar or other Che proteins. Swimming
behavior predicted by the simulation is characterized as tm = tumble (bias < 0.6), wt = wild type (bias 0.6-0.8), or sm = smooth (bias > 0.8).
b
The bct 1.1 model contains only one transducer (Tar), whereas E. coli actually has four transducer species. Thus, the behavior of modeled T-
Single null mutants
T- b
B-
W-
A-
Y-
Z-
Multiple null mutants
T-Z-
B-Z-
T-B-Z-
W-z-
T-W-Z-
A-Z-
Y-z-
B-Y-Z-
Overproduction mutants
T2+ b
B2+
w2+
“The simulated network accurately
reproduces the phenotype of more
than 30 mutants in which components
of the chemotactic pathway are deleted
and/or expressed in excess amounts”
of the enzymatic
alyzed kinetically.
ither singly or in
arge numbers and
ted. There is, in
e set of data, un-
g system, against
simulate the bio-
axis in a computer
ally reproduce all
behavioral obser-
e first step toward
simulation of the
ce receptor to the
id, excitatory re-
llents. The slower
ptor methylation,
deled previously
kura and Honda,
l.
ibed in this paper, we
carry the chemotactic
ellar motor.
ria such as Escherichia
he ability to modulate
imuli. This is achieved
, which carry signals
otor (Figure 1) (Bourret
h many details are still
n attractant or repellent
phosphorylation of an
Borkovich et al., 1989;
. The phosphorylated
te group to a second
1988). The product of
racts with the flagellar
ch produces "tumble"
otor in the absence of
lts in "run" swimming
Two other components
are CheW, which ap-
CheA phosphorylation
(sp)
Figure 1. Signal transduction pathway in bacterial chemotaxis.
Symbols within circles denote the Che proteins, in some cases phos-
phorylated, which carry the chemotactic signals from the receptor
protein Tar to the flagellar motor. Solid arrows represent phosphor-
ylation reactions. Dashed arrows indicate regulatory interactions. The
methylation reactions catalyzed by CheR and CheB (open arrows)
are not included in the computer simulation.
basis of the adaptation of this response. In consequence, we do not
expect the model to show the slow recovery of swimming behavior
characteristic of wild-type bacteria, but it should be able to reproduce
the short-term, impulsive responses of wild-type and mutant bacteria.
It should also reflect the short-term behavior of mutants in which the
demethylating enzyme CheB has been removed or overexpressed,
because the latter protein forms part of the flux of phosphate groups
in the excitatory pathway.
Theory
In this section we present the algorithms used to represent the signaling
reactions and the assumptions inherent in their use.
Information on the binding of aspartate or Ni2" to the Tar receptor
is relayed through the cytoplasm by changes in the concentrations of
various phosphorylated protein species. It is convenient to divide the
individual steps in this cascade into binding steps, in which two mo-
lecular components associate or disassociate, and reaction steps in
which a phosphate group is added or removed from a protein. Each
step is represented by an equation which is evaluated repeatedly at
intervals of time At, which is typically 5 ms.
Experimentally, the response of bacteria to a step change in attractant
or repellant concentration occurs in -0.2 s (Segall et al., 1982) and
evidence of a delay due to diffusion has been obtained only in ab-
ation of
rations
X [Lo]}
ins (see
At and
tein au-
tionship
ecies in
] is the
nd Km is
, 1984).
reacting
by
motor is
lt from
wise or
ss when
1). The
of CheY
iM, and
eraction
ransfer
simple
binding
uns and
complex
alculate
0.6 -
0
0
*~0.4-
coLo 10 20 30
CheYp (nM)
Figure 3. Proportion of time spent in run (l), tumble (0), and pause
(A) modes predicted for the flagellar motor model, with the resting
concentration of CheYp in a wild-type bacterium set to 9 nM.
tumbles of the flagellar motor. In the present simulation, this model
has been extended to incorporate two additional features. One is the
cooperativity between CheY levels and flagellar motor response seen
in mutant strains in which CheY is over-expressed to different levels
(Kuo and Koshland, 1989). The effective species is now thought to
be CheYp. The other feature added to the motor model is the capacity
for pausing, which has been reported by Eisenbach and colleagues
to be an inherent part of the behavioral repertoire of coliform bacteria
(Lapidus et al., 1988; Eisenbach et al., 1990).
These observations are incorporated into a model in which the motor
complex, denoted M, binds sequentially up to four CheYp molecules
thereby producing five distinct molecular species. Two of these (M
and MYp) rotate counterclockwise, another two (MYpYpYp and
MYpYpYpYp) rotate clockwise, and one species (MYpYp) is stationary,
corresponding to the pausing of the motor (Figure 2). In a population
of bacteria, the rotational bias (defined as the fraction of time spent
in the counterclockwise mode) is therefore given by the following:
bias = M + MYp
M + MYp + MYpYp + MYpYpYp + MYpYpYpYp
As in the earlier model of Block et al. (1982, 1983), transitions between
each pair of states are governed by paired first-order rate constants,
such as kl, and k2rYp (which is pseudo first-order at a given concen-
tration of Yp). For an individual motor, these rate constants represent
the probabilities per unit time of terminating the current state.
The Adair-Pauling model of a multisite allosteric enzyme allows
dissociation constants for each binding step to be calculated from two
parameters-the dissociation constant of binding of the first binding
site (K) and the factor (a) by which the affinity of successive binding
sites change (Segel, 1975). We have assumed that the flagellar motor
is similarly well-behaved and calculated K and a from an arbitrarily
assigned value for the resting (unstimulated) concentration of CheYp
and from the observed proportion of time spent in runs, pauses, and
tumbles of an unstimulated, wild-type bacterium (Lapidus et al., 1988;
Eisenbach et al., 1990). In trial simulations we found that the resting
concentration of CheYp could be varied over a wide range without
affecting overall performance. In the absence of experimental data
bearing on this value, we selected 9 nM, which in combination with
the best available values of reaction rates, produces an approximately
wild-type rotational bias for mutant strains lacking either Tar, CheW,

CHEMOTAXIS NETWORK FROM BIOMODELS
Biochemical
Diffusion(d+s)
Distance(d)
Adjacency(d+s)
Correlation(spearman)
0.00.20.40.60.81.0
First
neighbors
DistanceDiffusion
Topology
Direction
Sign
Kinetics
+
+
+
+
+
+
+
-
+
+
-
-
+
-
-
-
~90% accuracy of network models
Biochemical
Diffusion(d+s)
Distance(d)
Adjacency(d+s)
Correlation(spearman)
0.00.20.40.60.81.0
Accuracy

EXPERIMENTAL EVIDENCE
Phenotype
Gene
expression
A
-
,Yp
Y
-
,Yp
Z
-
,Yp
A
-
Z
-
,Yp
Y
-
Z
-
,Yp
B
-
Y
-
Z
-
,Yp
A2+
,Yp
T2+
,A
T2+
,TA
B2+
,Ap
B2+
,Yp
W2+
,A
W2+
,WA
Z2+
,Yp
Y2+
,Yp
Biochemical
Network
A
-
Y
-
Z
-
A
-
Z
-
Y
-
Z
-
B
-
Y
-
Z
-
A2+
T2+
B2+
W2+
Z2+
Y2+
Biochemical
Network
Sign
-1 0 1
Comparing predictions with experimental assays in bacterial chemotaxis
Perturbation of
gene X
Expression of gene Y
random
walk
biased
walk
a Wild-type
Phenotype
Observed
change
b c
86%
correct
75%
correct
Bacterial Chemotaxis Simulation
[Ro] and [Lo] are the total concentrations of receptor and ligand,
ively, [RL] is the concentration of the protein-ligand complex,
d is the dissociation constant of the complex.
ther binding steps in the simulation involve the association of
asmic proteins at comparable, and usually low, concentrations
r this we use the relationship:
0.5{[Ro] + [Lo] + Kd - V([Ro] + [Lo] + Kd)2 - 4[Ro] X [Lo]}
[Ro] and [Lo] are arbitrarily assigned to the two proteins (see
1975).
tion steps are not assumed to run to completion within At and
egrated by successive steps of the simulation. For protein au-
phorylations we use the simple Michaelis-Menten relationship
V J=[lkcat X [ATP]l
] [ATP] + Km
J
v is the rate of formation of the phosphorylated species in
per second, [E] is the free enzyme concentration, [ATP] is the
tration of ATP, k_cat is the catalytic rate constant and Km is
haelis constant (Michaelis and Menten, 1913; Fersht, 1984).
other reactions we assume that the concentrations of reacting
are small, so that the reactions are simply described by
v = (k.cat/Km) [E] X [S]
autodephosphorylations
v = klcat [E]
llar Motor
all other components of the simulation, the flagellar motor is
ingle molecular species. It is a complex structure, built from
imately 40 distinct proteins that rotates either clockwise or
rclockwise at speeds of .300 cycles/s (significantly less when
terium is tethered to a surface) (Jones and Aizawa, 1991). The
on of rotation is controlled by the phosphorylated form of CheY
h an interaction with the switch components (FliG, FliM, and
oteins) of the motor Uones and Aizawa, 1991). The interaction
s to be a simple binding of CheYp rather than a phosphotransfer
n (Bourret et al., 1990).
s shown previously by Block et al. (1982, 1983) that a simple
te model in which run and tumble states differ in the binding
ngle ligand has similar stochastic properties to the runs and
Different states ofthe flagellar motor (M) are
determined by the binding of CheYp (Yp):
State 'T M
State '3' MYp
State '5' MYpYp
State '7' MYpYpYp
State '9' MYpYpYpYp
ccw rotation
ccw rotation
zero rotation
cw rotation
cw rotation
(run)
(run)
(pause)
(tumble)
(tumble)
Occupation of the different states is governed by
reversible equilibria:
M + Yp q-b MYp
MYp + Yp W MYpYp
MYpYp + Yp W MYpYpYp
Ki = kil/klr
K2 = k2l/k2r
K3 = k3d/k3r
1.0
0.6 -
0
0
*~0.4-
coLo 10 20 30
CheYp (nM)
Figure 3. Proportion of time spent in run (l), tumble (0), and pause
(A) modes predicted for the flagellar motor model, with the resting
concentration of CheYp in a wild-type bacterium set to 9 nM.
tumbles of the flagellar motor. In the present simulation, this model
has been extended to incorporate two additional features. One is the
cooperativity between CheY levels and flagellar motor response seen
in mutant strains in which CheY is over-expressed to different levels
(Kuo and Koshland, 1989). The effective species is now thought to
be CheYp. The other feature added to the motor model is the capacity
for pausing, which has been reported by Eisenbach and colleagues
to be an inherent part of the behavioral repertoire of coliform bacteria
(Lapidus et al., 1988; Eisenbach et al., 1990).
These observations are incorporated into a model in which the motor
complex, denoted M, binds sequentially up to four CheYp molecules
thereby producing five distinct molecular species. Two of these (M
and MYp) rotate counterclockwise, another two (MYpYpYp and
MYpYpYpYp) rotate clockwise, and one species (MYpYp) is stationary,
corresponding to the pausing of the motor (Figure 2). In a population
of bacteria, the rotational bias (defined as the fraction of time spent
in the counterclockwise mode) is therefore given by the following:
bias = M + MYp
M + MYp + MYpYp + MYpYpYp + MYpYpYpYp
As in the earlier model of Block et al. (1982, 1983), transitions between
each pair of states are governed by paired first-order rate constants,
such as kl, and k2rYp (which is pseudo first-order at a given concen-
tration of Yp). For an individual motor, these rate constants represent
the probabilities per unit time of terminating the current state.
The Adair-Pauling model of a multisite allosteric enzyme allows
dissociation constants for each binding step to be calculated from two
parameters-the dissociation constant of binding of the first binding
site (K) and the factor (a) by which the affinity of successive binding
sites change (Segel, 1975). We have assumed that the flagellar motor
is similarly well-behaved and calculated K and a from an arbitrarily

I. TAKE HOME MESSAGE
1/3
kinetics
Marc Santolini - CCNR
Topology
Direction
Sign
Kinetics
+
+
+
+
+
+
+
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+
+
-
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-
-
Biochemical
Diffusion(d+s)
Distance(d)
Firstneighbors(d+s)
0.00.20.40.60.81.0
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interactome

PART II
NETWORK PERTURBATION BY MIRNAS
Ayşe Kılıç, Marc Santolini, Taiji Nakano, Matthias Schiller, Mizue Teranishi, Pascal Gellert, Yuliya
Ponomareva, Thomas Braun, Shizuka Uchida, Scott T. Weiss, Amitabh Sharma And Harald Renz, “A
Novel Systems Immunology Approach Identiﬁes The Collective Impact Of Five Mirnas In Th2
Inﬂammation"

R E V I E W S
mathematical properties of random networks14
.Their
much-investigated random network model assumes that
a fixed number of nodes are connected randomly to each
other(BOX2).Themostremarkablepropertyof themodel
is its‘democratic’or uniform character,characterizing the
degree,orconnectivity(k;BOX1),of theindividualnodes.
Because, in the model, the links are placed randomly
among the nodes,it is expected that some nodes collect
only a few links whereas others collect many more.In a
random network, the nodes degrees follow a Poisson
distribution, which indicates that most nodes have
roughly the same number of links,approximately equal
to the network’s average degree,<k> (where <> denotes
the average); nodes that have significantly more or less
linksthan<k>areabsentorveryrare(BOX2).
Despite its elegance, a series of recent findings indi-
cate that the random network model cannot explain
the topological properties of real networks. The
deviations from the random model have several key
signatures, the most striking being the finding that, in
contrast to the Poisson degree distribution, for many
social and technological networks the number of nodes
with a given degree follows a power law. That is, the
probability that a chosen node has exactly k links
follows P(k) ~ k –γ
, where γ is the degree exponent, with
its value for most networks being between 2 and 3
(REF.15).Networks that are characterized by a power-law
degree distribution are highly non-uniform, most of
the nodes have only a few links.A few nodes with a very
large number of links,which are often called hubs,hold
these nodes together. Networks with a power degree
distribution are called scale-free15
,a name that is rooted
Figure 2 | Yeast protein interaction network. A map of protein–protein interactions18
in
Saccharomyces cerevisiae, which is based on early yeast two-hybrid measurements23
, illustrates
that a few highly connected nodes (which are also known as hubs) hold the network together.
The largest cluster, which contains ~78% of all proteins, is shown. The colour of a node indicates
the phenotypic effect of removing the corresponding protein (red = lethal, green = non-lethal,
orange = slow growth, yellow = unknown). Reproduced with permission from REF.18 ©
Macmillan Magazines Ltd.
Jeong et al., “Lethality and centrality in protein networks“ Nature 2001
LETHALITY-CENTRALITY RULE

MIRNA TARGETS IN THE INTERACTOME

EXPRESSION DATA FROM ASTHMA MODEL
Naive
Th1-
acute
Th1-
chronic
Th2-
acute
Th2-
chronic
miRNA matrix mRNA matrix Protein matrix
Meta-dimensional analysis using human Interactome
Model: female BALB/c mice aged 6–8 weeks
Mice were sensitized by injections of 10 µg OVA grade VI

WORKFLOW
mRNA and miRNA
mice expression data
Th2 disease module
Human interactome and
miRNA network
5 highest impact
miRNAs
Validation in mouse
IDEAL approach
naive
early stable early stable
Th1 Th2
Low High
con-miR
isotype
miR-cocktail
IL-13
MFI
mRNA and miRNA
Th2 disease module
miRNA network
5 highest impact
miRNAs
Validation in mouse
IDEAL approach
1 2 5 10 20 50 100
012345
Number of miRNAs attacking
CollectiveImpact(CI)
RSBN1
LRP8
TSC1
AKAP8L
PRKCD
PRR11
IFNA4
BNIP3
MCPH1
MAFK
CCL17
DLG2
hsa-let-7e
PPP2R5E
ASTE1
ARNT2
ZNF276
XRCC4
TRIM24
ZNF277INPPL1
PILRA
LDHB
PDE1BC14orf1
RPL8
GCC2
C5
PCDH19
BACE2
BATF
CDON
INPP5A
DNAJB14
SH3RF2
CAMK2N1
C1orf94
hsa-mir-200c
HMOX2
SMARCD1
CPSF7
CNN3
POLI
SLC25A25
LTB
XRN2
SIAH2
TINF2
RAPGEF6
PBX3
hsa-let-7b
hsa-mir-107
SEC63
ATF7
CCR8
PTPN3
GRP
TMEM62
SMURF1
RPS10
RPS21
MAP3K12
ARMC8
ANXA4
CDK10
INPP4A
FBXW8
FBXL6
MAML3
MLEC
EPB41
MAPRE1
ELF2
BMPR1A
hsa-mir-138
DTL
RAG1
ATRN
hsa-mir-340
TAPT1
NARS2
MIPOL1PTGS2
LY75
RPS13
GALNT3USP24
ZCCHC2
ZNF653
CCR4
ERF
ENTPD6
PIGF
PARD3
ZBTB34
RIPK1RAPGEF1 KIAA1462
hsa-let-7a
UPF3A
hsa-mir-607
CA2
IL9RGAPVD1
FANCL
TKT
SMARCA5
GNPDA1
LAMC1
RXRA
NEK1
hsa-mir-575
AURKC
TNFRSF8NRBP1
COBLL1
hsa-mir-106b
SPINT1
ALDH3A2
SH3BGRL FBXL2
PSME1
STK25
H1F0
LAMA5
hsa-mir-665
ERN1
hsa-let-7i
STAT5A
TPI1
SLC25A46
HSD17B7
hsa-mir-194
FRMD6
ASH1L
AP1S2
DUSP2
MBTD1
DGKI
CHST11
F2RL1
MLKL
HMGXB3
GNB4
ANKRD29
LITAF
KLHL3
hsa-let-7d
FUCA2
CCDC101
PBX2
FRMD4B
hsa-mir-580
ADNP
LIMK1
P2RY1
ATG4B
GATA2
hsa-mir-18b
PCYT2
RGS9
RPS12
hsa-mir-15b
RNF24
IRF8
ETNK1
hsa-mir-21
EIF4G3
PLEKHO1
LFNG
BCL2L15DNAJB9
CA1
PRDM2
SAT1
BHLHB9 GAS7
FLRT2
RPL17 HPSE
STX11
MEOX2
IGF1RCCDC22CGGBP1
RPS16
USP14
CPSF3
MYO1D
ZMYND11
TRIM44
hsa-let-7c
hsa-mir-516a-5p
HDAC7
NEK7
ITGB8
TNFRSF14ZNF605
DCP2
PRDM4
AQP9
MEPCE
HMCN1
CTNNA1
TNFSF10
TCF12SDCCAG3
ZFYVE16
DIP2C
DUSP19
ASXL2
CHST15
hsa-mir-203
AXIN1
CAMKK2
VEZTBCL7B
MEF2A
FHL3
ANKRD17
hsa-mir-494
IL24
RASSF5
DENND4A
CREB3L2
PUM2
RNF130
TRIM8
UNKL
PHF20L1
SCMH1
hsa-mir-552
PYGM
NRP1
BRPF1
SERINC3
EIF5B
RANBP3
hsa-mir-149
CSK
NPAT GNB2L1LY9
CARM1
PCNA
hsa-mir-150
ST3GAL4
RB1 RREB1
HSPA2
CNOT1
RPL23
hsa-mir-17
F11R
ATP11C
hsa-mir-135b
MPP7
SRF
SMYD3
NOTCH1
NEDD8
ITM2A
hsa-mir-33b
STAR
LRIG1
ATP2B1
CCR7 TIGIT
CD84
RPL31
YWHAZ
TESK1
RNLS
C21orf91
CENPT
XPC
DYNLT3
ACTR6
LPIN2
RUNX2
KLKB1
CSGALNACT1
CHCHD3MIF4GD
PLEKHA1
EHMT1
RPL27A
SAMSN1
PSMD7
RORA
AKAP8
VTI1A
EPAS1
AKR1B1
DAPK1ZDHHC23
FBXW7
CCL1
IL1RN
GAPDHS
DEK
ADAM19
TDG
PLCB4
HSD3B1
WHSC1
MRPL30
RBM6
MDK
PPP4R1
SUV420H1
HSPA8
hsa-mir-623
RNF44
AMPH
LILRB4
USP9X
BZW2
RBM28
EDEM3
MYO10
GLDC
ASAP1
CHRM2 QTRT1
hsa-mir-383
KLHL6
VAT1
CDK6
ITPR1
KIAA1598
ATXN1
UBL3
hsa-let-7f
STOM
VDR
THADA
ZBTB44
KLHDC10
ARHGEF2
IPMK
YLPM1IFNG
STT3B
CTDSPL2
WDR82UBR1
hsa-mir-517a
QSER1
BRF1
SLC39A10
RALY
GRSF1
FN1
NCOR2
CDK12
GNAQ
ZNF654
GALC
UBE2D1
FFAR2
H2AFZ
TOM1
NCKAP1
GSRhsa-mir-220b
SERF2
GNG2RAF1
RGS19
SETD1A
IMPA2FGF18
ERAP1
STAG1
IKZF4
ELMOD2
PROS1
PLB1
STAMBPL1
ERC1
STXBP4PPARG
hsa-mir-154
ACVR2A
NEU1
TMTC2RPL18
IL9RPAP2 DCBLD2
IL19
HEPHL1
MID2
GIMAP8
CAMK2D
MYH9
TERF2DHRS7
KIFC1 hsa-mir-380
CYP1A1
ADH5
SRRT
EPN1
GNG4
PTPN4
PER1
ABCB8
DHX9
RGS3
hsa-mir-545
SLC48A1
PCGF2
RGS1
TXK
ADARB1
ASB13
AURKB
MPZL1
OVGP1
RHOH
TPD52
hsa-mir-143
RPS23
hsa-mir-99b
CNOT10
RAB11B
ATRNL1
USP15
WDFY2
RAB9A
hsa-mir-571
NAV2
ZRSR2
PHOX2A
ATXN7L2
RABGGTA
FGFR1OP2
NCLN
RAB8A
TTLL4
FOXJ3
ZMIZ2
ETS2
LAG3
EGLN3
ZNF318
UXT
CLDND1
TTBK1
CYCS
CRTAM
hsa-mir-30b
PCYT1A hsa-mir-200bATRIP DYRK2hsa-mir-328
NDUFA10
hsa-mir-505
TMEM159
hsa-mir-379
SMC5
RNF19A
GPRASP1
GABARAP
hsa-mir-24
EIF3K
SLC17A6
RPL19
CMPK1
TMEM30A
GRN
HIC1
TNFSF8
hsa-mir-30c
NBN
IL10
MAPK8
ADORA2B
CSNK1G3
MYO1E
LRP12
COIL
hsa-mir-603
FUT8
PPFIA1
hsa-mir-155
RPS6KA1
KPNA1
SIDT2
TRIM25
FDFT1
UBE2F
CTLA4
KIF3A
TARS2
hsa-mir-760
CCR3
LXN
KHDRBS1
MECR
PLCD1
HIVEP2
GLB1
RPL13A
GRIPAP1
MTOR
hsa-mir-148b
CHD7
DMXL1
TMOD4
INADL
NFX1
hsa-mir-422a
SH2D1A
DAG1
DOCK11
CLSTN1
INTS4
GZF1
RPL9
PARP4
RPS15A
SKP1
PLP2
ATP10A
hsa-mir-452
TAF8
HIPK1
MSH3
KLRG1
PDE2A
MLLT10
hsa-mir-181c
CCDC90B
SETX
CEP55
CNIH2
FRS2
FBXL5
MARS2
WDR33
U2AF2
TANK
PTPN12
ZBTB32
SPN
SLC25A27
IPCEF1
EP400
RGS13
hsa-mir-206ZNF592
PEX10
CWC15
hsa-mir-220c
PARD6G
DUSP4SUFU
JPH1
SH3PXD2A
S100A1
SLC44A1
SERPINF1
CYFIP1
USP28
FOXF2PRKAR2A VAMP4
GTF3C2 IL1R2
BAZ1Ahsa-mir-181a
MOCOS
FOXN2
ABCC1
MYO1C LSM1 hsa-mir-922
ETV5
PAN3 RBM41
STK24
SETD7
SPHK2
MLF1
RTN3
ANXA2
SLC7A8
EZR
RC3H1
hsa-mir-548a-3p
RPE
PCGF5
LPXN
TREX1 LYN
MED13
RBM38
MKL2
RPS9
MAP3K5hsa-mir-620
MCL1
CD5PTPN6
ARL14MAPKAPK5 VCL
LCORL
CSNK1E
SUV39H1
NEB
RPL10
hsa-mir-497
PTPRJ
APLP1
HIST1H3B
DDX11
ARL6
DVL1 RPL13
RNPC3
ARL5A
DDX28
RPSAZMYM4
hsa-mir-487a
ZBTB38
DICER1
PTGER2
ARHGEF3
hsa-mir-520h
THRAP3
IL1R1
ABL2
NDUFA3
IL23R
LAT
HIPK2
hsa-mir-103
ARFGEF1
ZRANB1
KIAA2026
PTPN13
hsa-mir-29b
CRMP1
APBB3
IPO9
hsa-mir-488
ARHGEF12
SPP1
RPLP2
LLGL1
EIF3F
LZTFL1
FAM91A1
RPS5
DDX19B
PLXNC1
GIT1
TPBG
UHRF2
INHBA
hsa-mir-211
S100A11
ACTN1
ACVR1
HIST1H4C
CNTNAP4
GYPC
POLR3F
GMNN
TAOK2
ARG1
THOC1
KIF16B
PHF20
TIPARP
PPM1F
PTP4A3
SFN
OLR1
SIN3A
BCL2L11
RAPH1
ICOS
hsa-mir-374b IKBKE
SAP130
AHR
TOP2A
GNPTAB
PJA1
FGL2
ADAM9
EFR3A
SETBP1
HN1L
NUMA1
STUB1
SLK
GAD1
SERTAD2
GJB3
PALLD
C19orf66
GRM5
SLCO3A1
PIK3CB
ESYT2
KAT2A
FBXW11hsa-mir-520f
SUSD2
CNOT2
PAIP2
TRAM1 NCAPH
ANKRD13C
S100A16
HIRA
AQR
NR3C1
hsa-mir-222
CA8
ZDHHC3
CAPG
MTMR2
hsa-mir-551b
ZBTB10
MKNK1
PTPRC
ADAP1
RHOQ
PTPLAD1
CA5B
DAXX
MPHOSPH8hsa-mir-200a
UGP2
EBAG9
IKBKB
RAB11FIP4
IPPK
PRDX6
CYP11A1
RXRB
ZNF25
RNF146
ITGAD
RAB11FIP1
RNF216
PLAUR
CAP2
CYP17A1
TEX2
PLK2
TIMP3
SMAD1
TIMP1
RASSF8
DUSP14
RASA3
IGF2R
MKRN2
ITGB2
DDX46
TRA2A ZBTB11
CRY2
RPS28
PPAP2B
STARD13
RNF31
GLUD1
SESN3
CDK11APRICKLE3
CMAS LEF1
PHACTR4
MFHAS1
SMAD3OSBPL3
MUS81
CASP6
MYCBP2
MYLIP
USP34
hsa-let-7g
PSIP1
MAPKAPK2
NUP153
CASP4
TRPS1
QRICH1
KIF2C
PRKCH
SVIL
AXL
CTCF
CD2AP
EYA1
SRSF2
RPTOR
hsa-mir-214
ITGA2DUSP16
INTS7
MPI
MARK3
SMARCD3BNC1
PARP8
ACBD5
RNF111
HBS1L
ICAM2
MTMR10
GNPAT
RCOR1
hsa-mir-18a
PHC1
SRXN1
CCR1
B4GALT5
CAPNS1
MAPK8IP3
MSC
CDC27
GPR126
IER3
QPCTL
GATM
HSF1
MFNG
hsa-mir-93PKD2
GATA3
TOR1AIP2
KLRD1
GRAMD3
RRAS2
CASP1
KBTBD2
CREBL2
CABIN1
RASL11A
IGBP1
SPRED2HIST1H3C
AMMECR1
hsa-mir-554
RYK
MIA3
COX7A2L
CABYR
PPFIBP1EPS15L1
MAX
hsa-mir-182
TEC
RPS7
STK38
LGALS3
LRRFIP1
RPL30
BMPR2
CD80
PTDSS1
LGALS1
TNKS2
FARP2
SLC24A3
MRAS
PTPN9
SDC4
TSC2
CAPN2
PIK3AP1
CD44
CYTH3
NEK6MED26
ERCC4
FAM57A
TAF4B
HEG1
UTRN
BCAS2
ARHGAP29
GAPDH
HLF
DPM1hsa-mir-181b
NEO1
LTN1 ZC3H12C
hsa-mir-25
hsa-mir-152PDCD10
IL5 RBPJ
RALGDS
FOXP4
hsa-mir-610
CBLB
BCAR3
PDSS1
NDUFB1
ATR
ENY2
GIGYF2
PICK1
hsa-mir-19a TRAF3IP3
KIF13A
POGZ
AKAP6
hsa-mir-326
ATP10B
IL13
ITGB1BP1
hsa-mir-192
PRPF38B
SERPINA3
IL2RB
CLCN5
hsa-mir-195
IRF3
SSFA2
VASP
CDK4
ENAH
TADA2B
MAP3K14
hsa-mir-649
TRIM35
PJA2
NCAPG2
PBRM1
EPN2
SMC4REV1
GPR45
PACSIN1
PUM1
hsa-mir-874
SFRP4
MAP2K7
TMEM132B
hsa-mir-26a
DIAPH3
CD200R1
CDO1
BCL9
F2R
HS3ST3B1
hsa-mir-106a
FOXP3
NT5E
LSRATG4D
ARHGEF7
hsa-mir-23a
CEP68
TXNDC17
CHRAC1MLXIP
ATP2B4 YTHDC2AHNAK SLC2A3
LCOR IFI16
ADCY3
JAG1
RAB31
PLCL1
RALGPS2
NPY
SUOX
SF3B4IGFBP7
CD27
hsa-mir-921
NDOR1
PHF3
RPL26hsa-mir-29a
XPO7
TAF7
TOP3A
CSTA
IMPDH1
hsa-mir-378
TAGAP
PCID2
NUP107
DGCR2
FBXL12
SOCS2
CSF1
CENPO
CRY1
CST3
KSR1
FGF2CNKSR3
MMP25
hsa-mir-32
UGCG
GTF2I
CLTA
SUCLG2
hsa-mir-625
IRF2BP1
FKBP3
hsa-mir-608
MAST1 SEMA4D
DCUN1D4
ERO1L
ZCCHC7
ST6GAL1
NDUFV2
GPM6B
MRPS24
STRBPRBM12
ZYG11B
RFK
CXCL10
hsa-mir-23bUBE2H
SLC35C1
TAGLN
APBB1IPMVP
GPATCH8
HLA-A
hsa-mir-132
NCK1
KIAA0922
POLD2
VPS54
CYP1B1
PIP4K2A
IRAK3
IKZF2
PANK4
hsa-mir-19b
STX1A
hsa-mir-425
ADPGK
UCHL3
AMBRA1
ID2
RPS29
ENTPD1
STAB1
hsa-mir-765
PLOD2FBXO4
TAF1D
UBP1
PRMT8
TSC22D4
hsa-mir-26b
PLCXD2
PRPF39
GAS2
PTPN5
MYNN
ATXN7L3
LTB4R
HS2ST1
ME1
NFIL3
DET1
hsa-mir-29c
NPNT
BTLA
HSPB1
R3HDM1
hsa-mir-448
GOT1
DHRS3
RAD50
SELL
ZFAND3
MAF
FLOT1
ETV6
DSTN
SCN1B
HOOK1
PDLIM5CHKA
CDKN2C
LYRM2
hsa-mir-636
MED1
hsa-mir-504
SLC4A2
hsa-mir-204
LGMN
ORMDL3
DOCK6
FNDC3A
KDSR
RAB20
ANGEL1
EML2
GNA15
IL22
NYNRIN
DIS3
hsa-mir-181d
TAF4
TBX21
MED18 GSN
RAB39B
FURIN
FES
hsa-mir-98
TASP1
CPNE1
SETDB2 CTDP1
ATG16L1
hsa-mir-30e
MPHOSPH9ZNF644
FAU
hsa-mir-216aTLE4
hsa-mir-27b
C2orf42
NDUFB6PEX13
ALDOA
RPL12
KLK3
KRTAP11-1
ELK3
DYRK1B
hsa-mir-936
SP1
EIF5
KLF12
PTK2Bhsa-mir-637
EDN1
IL17RBDCAF6
KCNK6
ANKRD12
ZNF281
PRR5L
CUL4B
ERGIC1
ZPBP2
PPP1R2 DENND5AKHSRP
TNFRSF1ACA13
ADAMTS6
HIP1
TMEM43
TRRAP
TSC22D1
XAB2
CASZ1
hsa-mir-593
hsa-mir-22
HSF2
SENP2
ATXN7L1
PTPN1
ACIN1
BTG2
STXBP1
hsa-mir-657
USP7
HAVCR2
IL12RB1
BOLL
RRM2
PICALM
CDK2
hsa-mir-20b
CD9
hsa-mir-449b RBBP9
PDSS2RASL11B
AGTPBP1 MBP
N4BP2 hsa-mir-372
GTF2A1
CCL19
MPRIP
GCNT1
SLC20A1
RNF138
N4BP1
SLC11A1
hsa-mir-766
DMXL2
NLRP3
ELL2LIN9
SMAD2
EZH2
DLG4
MYD88
ATP1B1GLRX
hsa-mir-7
DNAJC5B
WBP11DGKD
hsa-mir-301a
hsa-mir-363SF3B1
ARSB
FNTB
RNH1
hsa-mir-146a
BRWD1
CBFA2T2
SFPQIL6ST
IL10RA
ANGPTL2
B3GALT2
USP22
IL1RL1
RAB3GAP1
USE1
ID3
CBX7
CENPJ
DGKZ
PSME2
IFITM2
hsa-mir-302b
VRK2
PRDM1
CLCF1
NCKAP5
GOLGA1
ARL6IP6
hsa-mir-30a
PLCD4
TYMS
OGDH
STAT3
FBXW2
S100A6
ELK4
TIAM1
SDC1
hsa-mir-34a
RPL35A
FEZ2 PRSS8
SEC11A
NRF1
TWISTNB
FNBP4
EXOSC5
CRIM1
KLC1
C1GALT1
PKN2
HIST1H3HSUB1
VPS37A
LPHN2
KIF2A
AHDC1
PFKL
EMP1
ST6GALNAC2
TUBA4A
CD74
BACH2
ING2
FUNDC2
SRGAP3
MGRN1
EXOC6
TRAK2
DTYMK
PCSK1
SCGB1A1
CPD
RIOK3
CUL4A
CD38
hsa-mir-27a
PTBP2
CCR5
RNF128
hsa-mir-891b
GTF3A
CD79A
CTSH
NCOA3
FYN
CNOT6
IKZF3
PITPNM2
NKIRAS1
BCL3
NFS1
VAV1CLK2
ATXN2
SMG1
con-miR
isotype
miR-cocktail
IL-13
MFI mRNA and miRNA
Th2 disease module
miRNA network
5 highest impact
miRNAs
Validation in mouse
IDEAL approach
1 2 5 10 20 50 100
012345
RSBN1
LRP8
TSC1
AKAP8L
PRKCD
PRR11
IFNA4
BNIP3
MCPH1
MAFK
CCL17
DLG2
hsa-let-7e
PPP2R5E
ASTE1
ARNT2
ZNF276
XRCC4
TRIM24
ZNF277INPPL1
PILRA
LDHB
PDE1BC14orf1
RPL8
GCC2
C5
PCDH19
BACE2
BATF
CDON
INPP5A
DNAJB14
SH3RF2
CAMK2N1
C1orf94
hsa-mir-200c
HMOX2
SMARCD1
CPSF7
CNN3
POLI
SLC25A25
LTB
XRN2
SIAH2
TINF2
RAPGEF6
PBX3
hsa-let-7b
hsa-mir-107
SEC63
ATF7
CCR8
PTPN3
GRP
TMEM62
SMURF1
RPS10
RPS21
MAP3K12
ARMC8
ANXA4
CDK10
INPP4A
FBXW8
FBXL6
MAML3
MLEC
EPB41
MAPRE1
ELF2
BMPR1A
hsa-mir-138
DTL
RAG1
ATRN
hsa-mir-340
TAPT1
NARS2
MIPOL1PTGS2
LY75
RPS13
GALNT3USP24
ZCCHC2
ZNF653
CCR4
ERF
ENTPD6
PIGF
PARD3
ZBTB34
hsa-let-7a
UPF3A
hsa-mir-607
CA2
IL9RGAPVD1
FANCL
TKT
SMARCA5
GNPDA1
LAMC1
RXRA
NEK1
hsa-mir-575
AURKC
TNFRSF8NRBP1
COBLL1
hsa-mir-106b
SPINT1
ALDH3A2
SH3BGRL FBXL2
PSME1
STK25
H1F0
LAMA5
hsa-mir-665
ERN1
hsa-let-7i
STAT5A
TPI1
SLC25A46
HSD17B7
hsa-mir-194
FRMD6
ASH1L
AP1S2
DUSP2
MBTD1
DGKI
CHST11
F2RL1
MLKL
HMGXB3
GNB4
ANKRD29
LITAF
KLHL3
hsa-let-7d
FUCA2
CCDC101
PBX2
FRMD4B
hsa-mir-580
ADNP
LIMK1
P2RY1
ATG4B
GATA2
hsa-mir-18b
PCYT2
RGS9
RPS12
hsa-mir-15b
RNF24
IRF8
ETNK1
hsa-mir-21
EIF4G3
PLEKHO1
LFNG
BCL2L15DNAJB9
CA1
PRDM2
SAT1
BHLHB9 GAS7
FLRT2
RPL17 HPSE
STX11
MEOX2
IGF1RCCDC22CGGBP1
RPS16
USP14
CPSF3
MYO1D
ZMYND11
TRIM44
hsa-let-7c
hsa-mir-516a-5p
HDAC7
NEK7
ITGB8
TNFRSF14ZNF605
DCP2
PRDM4
AQP9
MEPCE
HMCN1
CTNNA1
TNFSF10
TCF12SDCCAG3
ZFYVE16
DIP2C
DUSP19
ASXL2
CHST15
hsa-mir-203
AXIN1
CAMKK2
VEZTBCL7B
MEF2A
FHL3
ANKRD17
hsa-mir-494
IL24
RASSF5
DENND4A
CREB3L2
PUM2
RNF130
TRIM8
UNKL
PHF20L1
SCMH1
hsa-mir-552
PYGM
NRP1
BRPF1
SERINC3
EIF5B
RANBP3
hsa-mir-149
CSK
NPAT GNB2L1LY9
CARM1
PCNA
hsa-mir-150
ST3GAL4
RB1 RREB1
HSPA2
CNOT1
RPL23
hsa-mir-17
F11R
ATP11C
hsa-mir-135b
MPP7
SRF
SMYD3
NOTCH1
NEDD8
ITM2A
hsa-mir-33b
STAR
LRIG1
ATP2B1
CCR7 TIGIT
CD84
RPL31
YWHAZ
TESK1
RNLS
C21orf91
CENPT
XPC
DYNLT3
ACTR6
LPIN2
RUNX2
KLKB1
CSGALNACT1
CHCHD3MIF4GD
PLEKHA1
EHMT1
RPL27A
SAMSN1
PSMD7
RORA
AKAP8
VTI1A
EPAS1
AKR1B1
DAPK1ZDHHC23
FBXW7
CCL1
IL1RN
GAPDHS
DEK
ADAM19
TDG
PLCB4
HSD3B1
WHSC1
MRPL30
RBM6
MDK
PPP4R1
SUV420H1
HSPA8
hsa-mir-623
RNF44
AMPH
LILRB4
USP9X
BZW2
RBM28
EDEM3
MYO10
GLDC
ASAP1
CHRM2 QTRT1
hsa-mir-383
KLHL6
VAT1
CDK6
ITPR1
KIAA1598
ATXN1
UBL3
hsa-let-7f
STOM
VDR
THADA
ZBTB44
KLHDC10
ARHGEF2
IPMK
YLPM1IFNG
STT3B
CTDSPL2
WDR82UBR1
hsa-mir-517a
QSER1
BRF1
SLC39A10
RALY
GRSF1
FN1
NCOR2
CDK12
GNAQ
ZNF654
GALC
UBE2D1
FFAR2
H2AFZ
TOM1
NCKAP1
GSRhsa-mir-220b
SERF2
GNG2RAF1
RGS19
SETD1A
IMPA2FGF18
ERAP1
STAG1
IKZF4
ELMOD2
PROS1
PLB1
STAMBPL1
ERC1
STXBP4PPARG
hsa-mir-154
ACVR2A
NEU1
TMTC2RPL18
IL9RPAP2 DCBLD2
IL19
HEPHL1
MID2
GIMAP8
CAMK2D
MYH9
TERF2DHRS7
KIFC1 hsa-mir-380
CYP1A1
ADH5
SRRT
EPN1
GNG4
PTPN4
PER1
ABCB8
DHX9
RGS3
hsa-mir-545
SLC48A1
PCGF2
RGS1
TXK
ADARB1
ASB13
AURKB
MPZL1
OVGP1
RHOH
TPD52
hsa-mir-143
RPS23
hsa-mir-99b
CNOT10
RAB11B
ATRNL1
USP15
WDFY2
RAB9A
hsa-mir-571
NAV2
ZRSR2
PHOX2A
ATXN7L2
RABGGTA
FGFR1OP2
NCLN
RAB8A
TTLL4
FOXJ3
ZMIZ2
ETS2
LAG3
EGLN3
ZNF318
UXT
CLDND1
TTBK1
CYCS
CRTAM
hsa-mir-30b
NDUFA10
hsa-mir-505
TMEM159
hsa-mir-379
SMC5
RNF19A
GPRASP1
GABARAP
hsa-mir-24
EIF3K
SLC17A6
RPL19
CMPK1
TMEM30A
GRN
HIC1
TNFSF8
hsa-mir-30c
NBN
IL10
MAPK8
ADORA2B
CSNK1G3
MYO1E
LRP12
COIL
hsa-mir-603
FUT8
PPFIA1
hsa-mir-155
RPS6KA1
KPNA1
SIDT2
TRIM25
FDFT1
UBE2F
CTLA4
KIF3A
TARS2
hsa-mir-760
CCR3
LXN
KHDRBS1
MECR
PLCD1
HIVEP2
GLB1
RPL13A
GRIPAP1
MTOR
hsa-mir-148b
CHD7
DMXL1
TMOD4
INADL
NFX1
hsa-mir-422a
SH2D1A
DAG1
DOCK11
CLSTN1
INTS4
GZF1
RPL9
PARP4
RPS15A
SKP1
PLP2
ATP10A
hsa-mir-452
TAF8
HIPK1
MSH3
KLRG1
PDE2A
MLLT10
hsa-mir-181c
CCDC90B
SETX
CEP55
CNIH2
FRS2
FBXL5
MARS2
WDR33
U2AF2
TANK
PTPN12
ZBTB32
SPN
SLC25A27
IPCEF1
EP400
RGS13
hsa-mir-206ZNF592
PEX10
CWC15
hsa-mir-220c
PARD6G
DUSP4SUFU
JPH1
SH3PXD2A
S100A1
SLC44A1
SERPINF1
CYFIP1
USP28
FOXF2PRKAR2A VAMP4
GTF3C2 IL1R2
BAZ1Ahsa-mir-181a
MOCOS
FOXN2
ABCC1
ETV5
PAN3 RBM41
STK24
SETD7
SPHK2
MLF1
RTN3
ANXA2
SLC7A8
EZR
RC3H1
hsa-mir-548a-3p
RPE
PCGF5
LPXN
TREX1 LYN
MED13
RBM38
MKL2
RPS9
MAP3K5hsa-mir-620
MCL1
CD5PTPN6
ARL14MAPKAPK5 VCL
LCORL
CSNK1E
SUV39H1
NEB
RPL10
hsa-mir-497
PTPRJ
APLP1
HIST1H3B
DDX11
ARL6
DVL1 RPL13
RNPC3
ARL5A
DDX28
RPSAZMYM4
hsa-mir-487a
ZBTB38
DICER1
PTGER2
ARHGEF3
hsa-mir-520h
THRAP3
IL1R1
ABL2
NDUFA3
IL23R
LAT
HIPK2
hsa-mir-103
ARFGEF1
ZRANB1
KIAA2026
PTPN13
hsa-mir-29b
CRMP1
APBB3
IPO9
hsa-mir-488
ARHGEF12
SPP1
RPLP2
LLGL1
EIF3F
LZTFL1
FAM91A1
RPS5
DDX19B
PLXNC1
GIT1
TPBG
UHRF2
INHBA
hsa-mir-211
S100A11
ACTN1
ACVR1
HIST1H4C
CNTNAP4
GYPC
POLR3F
GMNN
TAOK2
ARG1
THOC1
KIF16B
PHF20
TIPARP
PPM1F
PTP4A3
SFN
OLR1
SIN3A
BCL2L11
RAPH1
ICOS
hsa-mir-374b IKBKE
SAP130
AHR
TOP2A
GNPTAB
PJA1
FGL2
ADAM9
EFR3A
SETBP1
HN1L
NUMA1
STUB1
SLK
GAD1
SERTAD2
GJB3
PALLD
C19orf66
GRM5
SLCO3A1
PIK3CB
ESYT2
KAT2A
FBXW11hsa-mir-520f
SUSD2
CNOT2
PAIP2
TRAM1 NCAPH
ANKRD13C
S100A16
HIRA
AQR
NR3C1
hsa-mir-222
CA8
ZDHHC3
CAPG
MTMR2
hsa-mir-551b
ZBTB10
MKNK1
PTPRC
ADAP1
RHOQ
PTPLAD1
CA5B
DAXX
UGP2
EBAG9
IKBKB
RAB11FIP4
IPPK
PRDX6
CYP11A1
RXRB
ZNF25
RNF146
ITGAD
RAB11FIP1
RNF216
PLAUR
CAP2
CYP17A1
TEX2
PLK2
TIMP3
SMAD1
TIMP1
RASSF8
DUSP14
RASA3
IGF2R
MKRN2
ITGB2
DDX46
TRA2A ZBTB11
CRY2
RPS28
PPAP2B
STARD13
RNF31
GLUD1
SESN3
CDK11APRICKLE3
CMAS LEF1
PHACTR4
MFHAS1
SMAD3OSBPL3
MUS81
CASP6
MYCBP2
MYLIP
USP34
hsa-let-7g
PSIP1
MAPKAPK2
NUP153
CASP4
TRPS1
QRICH1
KIF2C
PRKCH
SVIL
AXL
CTCF
CD2AP
EYA1
SRSF2
RPTOR
hsa-mir-214
ITGA2DUSP16
INTS7
MPI
MARK3
SMARCD3BNC1
PARP8
ACBD5
RNF111
HBS1L
ICAM2
MTMR10
GNPAT
RCOR1
hsa-mir-18a
PHC1
SRXN1
CCR1
B4GALT5
CAPNS1
MAPK8IP3
MSC
CDC27
GPR126
IER3
QPCTL
GATM
HSF1
MFNG
hsa-mir-93PKD2
GATA3
TOR1AIP2
KLRD1
GRAMD3
RRAS2
CASP1
KBTBD2
CREBL2
CABIN1
RASL11A
IGBP1
SPRED2HIST1H3C
AMMECR1
hsa-mir-554
RYK
MIA3
COX7A2L
CABYR
PPFIBP1EPS15L1
MAX
hsa-mir-182
TEC
RPS7
STK38
LGALS3
LRRFIP1
RPL30
BMPR2
CD80
PTDSS1
LGALS1
TNKS2
FARP2
SLC24A3
MRAS
PTPN9
SDC4
TSC2
CAPN2
PIK3AP1
CD44
CYTH3
NEK6MED26
ERCC4
FAM57A
TAF4B
HEG1
UTRN
BCAS2
ARHGAP29
GAPDH
HLF
DPM1hsa-mir-181b
NEO1
LTN1 ZC3H12C
hsa-mir-25
hsa-mir-152PDCD10
IL5 RBPJ
RALGDS
FOXP4
hsa-mir-610
CBLB
BCAR3
PDSS1
NDUFB1
ATR
ENY2
GIGYF2
PICK1
KIF13A
POGZ
AKAP6
hsa-mir-326
ATP10B
IL13
ITGB1BP1
hsa-mir-192
PRPF38B
SERPINA3
IL2RB
CLCN5
hsa-mir-195
IRF3
SSFA2
VASP
CDK4
ENAH
TADA2B
MAP3K14
hsa-mir-649
TRIM35
PJA2
NCAPG2
PBRM1
EPN2
SMC4REV1
GPR45
PACSIN1
PUM1
hsa-mir-874
SFRP4
MAP2K7
TMEM132B
hsa-mir-26a
DIAPH3
CD200R1
CDO1
BCL9
F2R
HS3ST3B1
hsa-mir-106a
FOXP3
NT5E
LSRATG4D
ARHGEF7
hsa-mir-23a
CEP68
TXNDC17
CHRAC1MLXIP
LCOR IFI16
ADCY3
JAG1
RAB31
PLCL1
RALGPS2
NPY
SUOX
SF3B4IGFBP7
CD27
hsa-mir-921
NDOR1
PHF3
RPL26hsa-mir-29a
XPO7
TAF7
TOP3A
CSTA
IMPDH1
hsa-mir-378
TAGAP
PCID2
NUP107
DGCR2
FBXL12
SOCS2
CSF1
CENPO
CRY1
CST3
KSR1
FGF2CNKSR3
MMP25
hsa-mir-32
UGCG
GTF2I
CLTA
SUCLG2
hsa-mir-625
IRF2BP1
FKBP3
hsa-mir-608
MAST1 SEMA4D
DCUN1D4
ERO1L
ZCCHC7
ST6GAL1
NDUFV2
GPM6B
MRPS24
STRBPRBM12
ZYG11B
RFK
CXCL10
hsa-mir-23bUBE2H
SLC35C1
TAGLN
APBB1IPMVP
GPATCH8
HLA-A
hsa-mir-132
NCK1
KIAA0922
POLD2
VPS54
CYP1B1
PIP4K2A
IRAK3
IKZF2
PANK4
hsa-mir-19b
STX1A
hsa-mir-425
ADPGK
UCHL3
AMBRA1
ID2
RPS29
ENTPD1
STAB1
hsa-mir-765
PLOD2FBXO4
TAF1D
UBP1
PRMT8
TSC22D4
hsa-mir-26b
PLCXD2
PRPF39
GAS2
PTPN5
MYNN
ATXN7L3
LTB4R
HS2ST1
ME1
NFIL3
DET1
hsa-mir-29c
NPNT
BTLA
HSPB1
R3HDM1
hsa-mir-448
GOT1
DHRS3
RAD50
SELL
ZFAND3
MAF
FLOT1
ETV6
DSTN
SCN1B
HOOK1
PDLIM5CHKA
CDKN2C
LYRM2
hsa-mir-636
MED1
hsa-mir-504
SLC4A2
hsa-mir-204
LGMN
ORMDL3
DOCK6
FNDC3A
KDSR
RAB20
ANGEL1
EML2
GNA15
IL22
NYNRIN
DIS3
hsa-mir-181d
TAF4
TBX21
MED18 GSN
RAB39B
FURIN
FES
hsa-mir-98
TASP1
CPNE1
SETDB2 CTDP1
ATG16L1
hsa-mir-30e
MPHOSPH9ZNF644
FAU
hsa-mir-216aTLE4
hsa-mir-27b
C2orf42
NDUFB6PEX13
ALDOA
RPL12
KLK3
KRTAP11-1
ELK3
DYRK1B
hsa-mir-936
SP1
EIF5
KLF12
PTK2Bhsa-mir-637
EDN1
IL17RBDCAF6
KCNK6
ANKRD12
ZNF281
PRR5L
CUL4B
ERGIC1
ZPBP2
PPP1R2 DENND5AKHSRP
TNFRSF1ACA13
ADAMTS6
HIP1
TMEM43
TRRAP
TSC22D1
XAB2
CASZ1
hsa-mir-593
hsa-mir-22
HSF2
SENP2
ATXN7L1
PTPN1
ACIN1
BTG2
STXBP1
hsa-mir-657
USP7
HAVCR2
IL12RB1
BOLL
RRM2
PICALM
CDK2
hsa-mir-20b
CD9
hsa-mir-449b RBBP9
PDSS2RASL11B
AGTPBP1 MBP
N4BP2 hsa-mir-372
GTF2A1
CCL19
MPRIP
GCNT1
SLC20A1
RNF138
N4BP1
SLC11A1
hsa-mir-766
DMXL2
NLRP3
ELL2LIN9
SMAD2
EZH2
DLG4
MYD88
ATP1B1GLRX
hsa-mir-7
DNAJC5B
WBP11DGKD
hsa-mir-301a
hsa-mir-363SF3B1
ARSB
FNTB
RNH1
hsa-mir-146a
BRWD1
CBFA2T2
SFPQIL6ST
IL10RA
ANGPTL2
B3GALT2
USP22
IL1RL1
RAB3GAP1
USE1
ID3
CBX7
CENPJ
DGKZ
PSME2
IFITM2
hsa-mir-302b
VRK2
PRDM1
CLCF1
NCKAP5
GOLGA1
ARL6IP6
hsa-mir-30a
PLCD4
TYMS
OGDH
STAT3
FBXW2
S100A6
ELK4
TIAM1
SDC1
hsa-mir-34a
RPL35A
FEZ2 PRSS8
SEC11A
NRF1
TWISTNB
FNBP4
EXOSC5
CRIM1
KLC1
C1GALT1
PKN2
HIST1H3HSUB1
VPS37A
LPHN2
KIF2A
AHDC1
PFKL
EMP1
ST6GALNAC2
TUBA4A
CD74
BACH2
ING2
FUNDC2
SRGAP3
MGRN1
EXOC6
TRAK2
DTYMK
PCSK1
SCGB1A1
CPD
RIOK3
CUL4A
CD38
hsa-mir-27a
PTBP2
CCR5
RNF128
hsa-mir-891b
GTF3A
CD79A
CTSH
NCOA3
FYN
CNOT6
IKZF3
PITPNM2
NKIRAS1
BCL3
NFS1
VAV1CLK2
ATXN2
SMG1
con-miR
isotype
miR-cocktail
IL-13
MFI
mRNA and miRNA
Th2 disease module
miRNA network
5 highest impact
miRNAs
Validation in mouse
IDEAL approach
1 2 5 10 20 50 100
012345
RSBN1
LRP8
TSC1
AKAP8L
PRKCD
PRR11
IFNA4
BNIP3
MCPH1
MAFK
CCL17
DLG2
hsa-let-7e
PPP2R5E
ASTE1
ARNT2
ZNF276
XRCC4
TRIM24
ZNF277INPPL1
PILRA
LDHB
PDE1BC14orf1
RPL8
GCC2
C5
PCDH19
BACE2
BATF
CDON
INPP5A
DNAJB14
SH3RF2
CAMK2N1
C1orf94
hsa-mir-200c
HMOX2
SMARCD1
CPSF7
CNN3
POLI
SLC25A25
LTB
XRN2
SIAH2
TINF2
RAPGEF6
PBX3
hsa-let-7b
hsa-mir-107
SEC63
ATF7
CCR8
PTPN3
GRP
TMEM62
SMURF1
RPS10
RPS21
MAP3K12
ARMC8
ANXA4
CDK10
INPP4A
FBXW8
FBXL6
MAML3
MLEC
EPB41
MAPRE1
ELF2
BMPR1A
hsa-mir-138
DTL
RAG1
ATRN
hsa-mir-340
TAPT1
NARS2
MIPOL1PTGS2
LY75
RPS13
GALNT3USP24
ZCCHC2
ZNF653
CCR4
ERF
ENTPD6
PIGF
PARD3
ZBTB34
hsa-let-7a
UPF3A
hsa-mir-607
CA2
IL9RGAPVD1
FANCL
TKT
SMARCA5
GNPDA1
LAMC1
RXRA
NEK1
hsa-mir-575
AURKC
TNFRSF8NRBP1
COBLL1
hsa-mir-106b
SPINT1
ALDH3A2
SH3BGRL FBXL2
PSME1
STK25
H1F0
LAMA5
hsa-mir-665
ERN1
hsa-let-7i
STAT5A
TPI1
SLC25A46
HSD17B7
hsa-mir-194
FRMD6
ASH1L
AP1S2
DUSP2
MBTD1
DGKI
CHST11
F2RL1
MLKL
HMGXB3
GNB4
ANKRD29
LITAF
KLHL3
hsa-let-7d
FUCA2
CCDC101
PBX2
FRMD4B
hsa-mir-580
ADNP
LIMK1
P2RY1
ATG4B
GATA2
hsa-mir-18b
PCYT2
RGS9
RPS12
hsa-mir-15b
RNF24
IRF8
ETNK1
hsa-mir-21
EIF4G3
PLEKHO1
LFNG
BCL2L15DNAJB9
CA1
PRDM2
SAT1
BHLHB9 GAS7
FLRT2
RPL17 HPSE
STX11
MEOX2
IGF1RCCDC22CGGBP1
RPS16
USP14
CPSF3
MYO1D
ZMYND11
TRIM44
hsa-let-7c
hsa-mir-516a-5p
HDAC7
NEK7
ITGB8
TNFRSF14ZNF605
DCP2
PRDM4
AQP9
MEPCE
HMCN1
CTNNA1
TNFSF10
TCF12SDCCAG3
ZFYVE16
DIP2C
DUSP19
ASXL2
CHST15
hsa-mir-203
AXIN1
CAMKK2
VEZTBCL7B
MEF2A
FHL3
ANKRD17
hsa-mir-494
IL24
RASSF5
DENND4A
CREB3L2
PUM2
RNF130
TRIM8
UNKL
PHF20L1
SCMH1
hsa-mir-552
PYGM
NRP1
BRPF1
SERINC3
EIF5B
RANBP3
hsa-mir-149
CSK
NPAT GNB2L1LY9
CARM1
PCNA
hsa-mir-150
ST3GAL4
RB1 RREB1
HSPA2
CNOT1
RPL23
hsa-mir-17
F11R
ATP11C
hsa-mir-135b
MPP7
SRF
SMYD3
NOTCH1
NEDD8
ITM2A
hsa-mir-33b
STAR
LRIG1
ATP2B1
CCR7 TIGIT
CD84
RPL31
YWHAZ
TESK1
RNLS
C21orf91
CENPT
XPC
DYNLT3
ACTR6
LPIN2
RUNX2
KLKB1
CSGALNACT1
CHCHD3MIF4GD
PLEKHA1
EHMT1
RPL27A
SAMSN1
PSMD7
RORA
AKAP8
VTI1A
EPAS1
AKR1B1
DAPK1ZDHHC23
FBXW7
CCL1
IL1RN
GAPDHS
DEK
ADAM19
TDG
PLCB4
HSD3B1
WHSC1
MRPL30
RBM6
MDK
PPP4R1
SUV420H1
HSPA8
hsa-mir-623
RNF44
AMPH
LILRB4
USP9X
BZW2
RBM28
EDEM3
MYO10
GLDC
ASAP1
CHRM2 QTRT1
hsa-mir-383
KLHL6
VAT1
CDK6
ITPR1
KIAA1598
ATXN1
UBL3
hsa-let-7f
STOM
VDR
THADA
ZBTB44
KLHDC10
ARHGEF2
IPMK
YLPM1IFNG
STT3B
CTDSPL2
WDR82UBR1
hsa-mir-517a
QSER1
BRF1
SLC39A10
RALY
GRSF1
FN1
NCOR2
CDK12
GNAQ
ZNF654
GALC
UBE2D1
FFAR2
H2AFZ
TOM1
NCKAP1
GSRhsa-mir-220b
SERF2
GNG2RAF1
RGS19
SETD1A
IMPA2FGF18
ERAP1
STAG1
IKZF4
ELMOD2
PROS1
PLB1
STAMBPL1
ERC1
STXBP4PPARG
hsa-mir-154
ACVR2A
NEU1
TMTC2RPL18
IL9RPAP2 DCBLD2
IL19
HEPHL1
MID2
GIMAP8
CAMK2D
MYH9
TERF2DHRS7
KIFC1 hsa-mir-380
CYP1A1
ADH5
SRRT
EPN1
GNG4
PTPN4
PER1
ABCB8
DHX9
RGS3
hsa-mir-545
SLC48A1
PCGF2
RGS1
TXK
ADARB1
ASB13
AURKB
MPZL1
OVGP1
RHOH
TPD52
hsa-mir-143
RPS23
hsa-mir-99b
CNOT10
RAB11B
ATRNL1
USP15
WDFY2
RAB9A
hsa-mir-571
NAV2
ZRSR2
PHOX2A
ATXN7L2
RABGGTA
FGFR1OP2
NCLN
RAB8A
TTLL4
FOXJ3
ZMIZ2
ETS2
LAG3
EGLN3
ZNF318
UXT
CLDND1
TTBK1
CYCS
CRTAM
hsa-mir-30b
NDUFA10
hsa-mir-505
TMEM159
hsa-mir-379
SMC5
RNF19A
GPRASP1
GABARAP
hsa-mir-24
EIF3K
SLC17A6
RPL19
CMPK1
TMEM30A
GRN
HIC1
TNFSF8
hsa-mir-30c
NBN
IL10
MAPK8
ADORA2B
CSNK1G3
MYO1E
LRP12
COIL
hsa-mir-603
FUT8
PPFIA1
hsa-mir-155
RPS6KA1
KPNA1
SIDT2
TRIM25
FDFT1
UBE2F
CTLA4
KIF3A
TARS2
hsa-mir-760
CCR3
LXN
KHDRBS1
MECR
PLCD1
HIVEP2
GLB1
RPL13A
GRIPAP1
MTOR
hsa-mir-148b
CHD7
DMXL1
TMOD4
INADL
NFX1
hsa-mir-422a
SH2D1A
DAG1
DOCK11
CLSTN1
INTS4
GZF1
RPL9
PARP4
RPS15A
SKP1
PLP2
ATP10A
hsa-mir-452
TAF8
HIPK1
MSH3
KLRG1
PDE2A
MLLT10
hsa-mir-181c
CCDC90B
SETX
CEP55
CNIH2
FRS2
FBXL5
MARS2
WDR33
U2AF2
TANK
PTPN12
ZBTB32
SPN
SLC25A27
IPCEF1
EP400
RGS13
hsa-mir-206ZNF592
PEX10
CWC15
hsa-mir-220c
PARD6G
DUSP4SUFU
JPH1
SH3PXD2A
S100A1
SLC44A1
SERPINF1
CYFIP1
USP28
FOXF2PRKAR2A VAMP4
GTF3C2 IL1R2
BAZ1Ahsa-mir-181a
MOCOS
FOXN2
ABCC1
ETV5
PAN3 RBM41
STK24
SETD7
SPHK2
MLF1
RTN3
ANXA2
SLC7A8
EZR
RC3H1
hsa-mir-548a-3p
RPE
PCGF5
LPXN
TREX1 LYN
MED13
RBM38
MKL2
RPS9
MAP3K5hsa-mir-620
MCL1
CD5PTPN6
ARL14MAPKAPK5 VCL
LCORL
CSNK1E
SUV39H1
NEB
RPL10
hsa-mir-497
PTPRJ
APLP1
HIST1H3B
DDX11
ARL6
DVL1 RPL13
RNPC3
ARL5A
DDX28
RPSAZMYM4
hsa-mir-487a
ZBTB38
DICER1
PTGER2
ARHGEF3
hsa-mir-520h
THRAP3
IL1R1
ABL2
NDUFA3
IL23R
LAT
HIPK2
hsa-mir-103
ARFGEF1
ZRANB1
KIAA2026
PTPN13
hsa-mir-29b
CRMP1
APBB3
IPO9
hsa-mir-488
ARHGEF12
SPP1
RPLP2
LLGL1
EIF3F
LZTFL1
FAM91A1
RPS5
DDX19B
PLXNC1
GIT1
TPBG
UHRF2
INHBA
hsa-mir-211
S100A11
ACTN1
ACVR1
HIST1H4C
CNTNAP4
GYPC
POLR3F
GMNN
TAOK2
ARG1
THOC1
KIF16B
PHF20
TIPARP
PPM1F
PTP4A3
SFN
OLR1
SIN3A
BCL2L11
RAPH1
ICOS
hsa-mir-374b IKBKE
SAP130
AHR
TOP2A
GNPTAB
PJA1
FGL2
ADAM9
EFR3A
SETBP1
HN1L
NUMA1
STUB1
SLK
GAD1
SERTAD2
GJB3
PALLD
C19orf66
GRM5
SLCO3A1
PIK3CB
ESYT2
KAT2A
FBXW11hsa-mir-520f
SUSD2
CNOT2
PAIP2
TRAM1 NCAPH
ANKRD13C
S100A16
HIRA
AQR
NR3C1
hsa-mir-222
CA8
ZDHHC3
CAPG
MTMR2
hsa-mir-551b
ZBTB10
MKNK1
PTPRC
ADAP1
RHOQ
PTPLAD1
CA5B
DAXX
UGP2
EBAG9
IKBKB
RAB11FIP4
IPPK
PRDX6
CYP11A1
RXRB
ZNF25
RNF146
ITGAD
RAB11FIP1
RNF216
PLAUR
CAP2
CYP17A1
TEX2
PLK2
TIMP3
SMAD1
TIMP1
RASSF8
DUSP14
RASA3
IGF2R
MKRN2
ITGB2
DDX46
TRA2A ZBTB11
CRY2
RPS28
PPAP2B
STARD13
RNF31
GLUD1
SESN3
CDK11APRICKLE3
CMAS LEF1
PHACTR4
MFHAS1
SMAD3OSBPL3
MUS81
CASP6
MYCBP2
MYLIP
USP34
hsa-let-7g
PSIP1
MAPKAPK2
NUP153
CASP4
TRPS1
QRICH1
KIF2C
PRKCH
SVIL
AXL
CTCF
CD2AP
EYA1
SRSF2
RPTOR
hsa-mir-214
ITGA2DUSP16
INTS7
MPI
MARK3
SMARCD3BNC1
PARP8
ACBD5
RNF111
HBS1L
ICAM2
MTMR10
GNPAT
RCOR1
hsa-mir-18a
PHC1
SRXN1
CCR1
B4GALT5
CAPNS1
MAPK8IP3
MSC
CDC27
GPR126
IER3
QPCTL
GATM
HSF1
MFNG
hsa-mir-93PKD2
GATA3
TOR1AIP2
KLRD1
GRAMD3
RRAS2
CASP1
KBTBD2
CREBL2
CABIN1
RASL11A
IGBP1
SPRED2HIST1H3C
AMMECR1
hsa-mir-554
RYK
MIA3
COX7A2L
CABYR
PPFIBP1EPS15L1
MAX
hsa-mir-182
TEC
RPS7
STK38
LGALS3
LRRFIP1
RPL30
BMPR2
CD80
PTDSS1
LGALS1
TNKS2
FARP2
SLC24A3
MRAS
PTPN9
SDC4
TSC2
CAPN2
PIK3AP1
CD44
CYTH3
NEK6MED26
ERCC4
FAM57A
TAF4B
HEG1
UTRN
BCAS2
ARHGAP29
GAPDH
HLF
DPM1hsa-mir-181b
NEO1
LTN1 ZC3H12C
hsa-mir-25
hsa-mir-152PDCD10
IL5 RBPJ
RALGDS
FOXP4
hsa-mir-610
CBLB
BCAR3
PDSS1
NDUFB1
ATR
ENY2
GIGYF2
PICK1
KIF13A
POGZ
AKAP6
hsa-mir-326
ATP10B
IL13
ITGB1BP1
hsa-mir-192
PRPF38B
SERPINA3
IL2RB
CLCN5
hsa-mir-195
IRF3
SSFA2
VASP
CDK4
ENAH
TADA2B
MAP3K14
hsa-mir-649
TRIM35
PJA2
NCAPG2
PBRM1
EPN2
SMC4REV1
GPR45
PACSIN1
PUM1
hsa-mir-874
SFRP4
MAP2K7
TMEM132B
hsa-mir-26a
DIAPH3
CD200R1
CDO1
BCL9
F2R
HS3ST3B1
hsa-mir-106a
FOXP3
NT5E
LSRATG4D
ARHGEF7
hsa-mir-23a
CEP68
TXNDC17
CHRAC1MLXIP
LCOR IFI16
ADCY3
JAG1
RAB31
PLCL1
RALGPS2
NPY
SUOX
SF3B4IGFBP7
CD27
hsa-mir-921
NDOR1
PHF3
RPL26hsa-mir-29a
XPO7
TAF7
TOP3A
CSTA
IMPDH1
hsa-mir-378
TAGAP
PCID2
NUP107
DGCR2
FBXL12
SOCS2
CSF1
CENPO
CRY1
CST3
KSR1
FGF2CNKSR3
MMP25
hsa-mir-32
UGCG
GTF2I
CLTA
SUCLG2
hsa-mir-625
IRF2BP1
FKBP3
hsa-mir-608
MAST1 SEMA4D
DCUN1D4
ERO1L
ZCCHC7
ST6GAL1
NDUFV2
GPM6B
MRPS24
STRBPRBM12
ZYG11B
RFK
CXCL10
hsa-mir-23bUBE2H
SLC35C1
TAGLN
APBB1IPMVP
GPATCH8
HLA-A
hsa-mir-132
NCK1
KIAA0922
POLD2
VPS54
CYP1B1
PIP4K2A
IRAK3
IKZF2
PANK4
hsa-mir-19b
STX1A
hsa-mir-425
ADPGK
UCHL3
AMBRA1
ID2
RPS29
ENTPD1
STAB1
hsa-mir-765
PLOD2FBXO4
TAF1D
UBP1
PRMT8
TSC22D4
hsa-mir-26b
PLCXD2
PRPF39
GAS2
PTPN5
MYNN
ATXN7L3
LTB4R
HS2ST1
ME1
NFIL3
DET1
hsa-mir-29c
NPNT
BTLA
HSPB1
R3HDM1
hsa-mir-448
GOT1
DHRS3
RAD50
SELL
ZFAND3
MAF
FLOT1
ETV6
DSTN
SCN1B
HOOK1
PDLIM5CHKA
CDKN2C
LYRM2
hsa-mir-636
MED1
hsa-mir-504
SLC4A2
hsa-mir-204
LGMN
ORMDL3
DOCK6
FNDC3A
KDSR
RAB20
ANGEL1
EML2
GNA15
IL22
NYNRIN
DIS3
hsa-mir-181d
TAF4
TBX21
MED18 GSN
RAB39B
FURIN
FES
hsa-mir-98
TASP1
CPNE1
SETDB2 CTDP1
ATG16L1
hsa-mir-30e
MPHOSPH9ZNF644
FAU
hsa-mir-216aTLE4
hsa-mir-27b
C2orf42
NDUFB6PEX13
ALDOA
RPL12
KLK3
KRTAP11-1
ELK3
DYRK1B
hsa-mir-936
SP1
EIF5
KLF12
PTK2Bhsa-mir-637
EDN1
IL17RBDCAF6
KCNK6
ANKRD12
ZNF281
PRR5L
CUL4B
ERGIC1
ZPBP2
PPP1R2 DENND5AKHSRP
TNFRSF1ACA13
ADAMTS6
HIP1
TMEM43
TRRAP
TSC22D1
XAB2
CASZ1
hsa-mir-593
hsa-mir-22
HSF2
SENP2
ATXN7L1
PTPN1
ACIN1
BTG2
STXBP1
hsa-mir-657
USP7
HAVCR2
IL12RB1
BOLL
RRM2
PICALM
CDK2
hsa-mir-20b
CD9
hsa-mir-449b RBBP9
PDSS2RASL11B
AGTPBP1 MBP
N4BP2 hsa-mir-372
GTF2A1
CCL19
MPRIP
GCNT1
SLC20A1
RNF138
N4BP1
SLC11A1
hsa-mir-766
DMXL2
NLRP3
ELL2LIN9
SMAD2
EZH2
DLG4
MYD88
ATP1B1GLRX
hsa-mir-7
DNAJC5B
WBP11DGKD
hsa-mir-301a
hsa-mir-363SF3B1
ARSB
FNTB
RNH1
hsa-mir-146a
BRWD1
CBFA2T2
SFPQIL6ST
IL10RA
ANGPTL2
B3GALT2
USP22
IL1RL1
RAB3GAP1
USE1
ID3
CBX7
CENPJ
DGKZ
PSME2
IFITM2
hsa-mir-302b
VRK2
PRDM1
CLCF1
NCKAP5
GOLGA1
ARL6IP6
hsa-mir-30a
PLCD4
TYMS
OGDH
STAT3
FBXW2
S100A6
ELK4
TIAM1
SDC1
hsa-mir-34a
RPL35A
FEZ2 PRSS8
SEC11A
NRF1
TWISTNB
FNBP4
EXOSC5
CRIM1
KLC1
C1GALT1
PKN2
HIST1H3HSUB1
VPS37A
LPHN2
KIF2A
AHDC1
PFKL
EMP1
ST6GALNAC2
TUBA4A
CD74
BACH2
ING2
FUNDC2
SRGAP3
MGRN1
EXOC6
TRAK2
DTYMK
PCSK1
SCGB1A1
CPD
RIOK3
CUL4A
CD38
hsa-mir-27a
PTBP2
CCR5
RNF128
hsa-mir-891b
GTF3A
CD79A
CTSH
NCOA3
FYN
CNOT6
IKZF3
PITPNM2
NKIRAS1
BCL3
NFS1
VAV1CLK2
ATXN2
SMG1
con-miR
isotype
miR-cocktail
IL-13
MFI
mRNA and miRNA
Th2 disease module
miRNA network
5 highest impact
miRNAs
Validation in mouse
IDEAL approach
1 2 5 10 20 50 100
012345
RSBN1
LRP8
TSC1
AKAP8L
PRKCD
PRR11
IFNA4
BNIP3
MCPH1
MAFK
CCL17
DLG2
hsa-let-7e
PPP2R5E
ASTE1
ARNT2
ZNF276
XRCC4
TRIM24
ZNF277INPPL1
PILRA
LDHB
PDE1BC14orf1
RPL8
GCC2
C5
PCDH19
BACE2
BATF
CDON
INPP5A
DNAJB14
SH3RF2
CAMK2N1
C1orf94
hsa-mir-200c
HMOX2
SMARCD1
CPSF7
CNN3
POLI
SLC25A25
LTB
XRN2
SIAH2
TINF2
RAPGEF6
PBX3
hsa-let-7b
hsa-mir-107
SEC63
ATF7
CCR8
PTPN3
GRP
TMEM62
SMURF1
RPS10
RPS21
MAP3K12
ARMC8
ANXA4
CDK10
INPP4A
FBXW8
FBXL6
MAML3
MLEC
EPB41
MAPRE1
ELF2
BMPR1A
hsa-mir-138
DTL
RAG1
ATRN
hsa-mir-340
TAPT1
NARS2
MIPOL1PTGS2
LY75
RPS13
GALNT3USP24
ZCCHC2
ZNF653
CCR4
ERF
ENTPD6
PIGF
PARD3
ZBTB34
hsa-let-7a
UPF3A
hsa-mir-607
CA2
IL9RGAPVD1
FANCL
TKT
SMARCA5
GNPDA1
LAMC1
RXRA
NEK1
hsa-mir-575
AURKC
TNFRSF8NRBP1
COBLL1
hsa-mir-106b
SPINT1
ALDH3A2
SH3BGRL FBXL2
PSME1
STK25
H1F0
LAMA5
hsa-mir-665
ERN1
hsa-let-7i
STAT5A
TPI1
SLC25A46
HSD17B7
hsa-mir-194
FRMD6
ASH1L
AP1S2
DUSP2
MBTD1
DGKI
CHST11
F2RL1
MLKL
HMGXB3
GNB4
ANKRD29
LITAF
KLHL3
hsa-let-7d
FUCA2
CCDC101
PBX2
FRMD4B
hsa-mir-580
ADNP
LIMK1
P2RY1
ATG4B
GATA2
hsa-mir-18b
PCYT2
RGS9
RPS12
hsa-mir-15b
RNF24
IRF8
ETNK1
hsa-mir-21
EIF4G3
PLEKHO1
LFNG
BCL2L15DNAJB9
CA1
PRDM2
SAT1
BHLHB9 GAS7
FLRT2
RPL17 HPSE
STX11
MEOX2
IGF1RCCDC22CGGBP1
RPS16
USP14
CPSF3
MYO1D
ZMYND11
TRIM44
hsa-let-7c
hsa-mir-516a-5p
HDAC7
NEK7
ITGB8
TNFRSF14ZNF605
DCP2
PRDM4
AQP9
MEPCE
HMCN1
CTNNA1
TNFSF10
TCF12SDCCAG3
ZFYVE16
DIP2C
DUSP19
ASXL2
CHST15
hsa-mir-203
AXIN1
CAMKK2
VEZTBCL7B
MEF2A
FHL3
ANKRD17
hsa-mir-494
IL24
RASSF5
DENND4A
CREB3L2
PUM2
RNF130
TRIM8
UNKL
PHF20L1
SCMH1
hsa-mir-552
PYGM
NRP1
BRPF1
SERINC3
EIF5B
RANBP3
hsa-mir-149
CSK
NPAT GNB2L1LY9
CARM1
PCNA
hsa-mir-150
ST3GAL4
RB1 RREB1
HSPA2
CNOT1
RPL23
hsa-mir-17
F11R
ATP11C
hsa-mir-135b
MPP7
SRF
SMYD3
NOTCH1
NEDD8
ITM2A
hsa-mir-33b
STAR
LRIG1
ATP2B1
CCR7 TIGIT
CD84
RPL31
YWHAZ
TESK1
RNLS
C21orf91
CENPT
XPC
DYNLT3
ACTR6
LPIN2
RUNX2
KLKB1
CSGALNACT1
CHCHD3MIF4GD
PLEKHA1
EHMT1
RPL27A
SAMSN1
PSMD7
RORA
AKAP8
VTI1A
EPAS1
AKR1B1
DAPK1ZDHHC23
FBXW7
CCL1
IL1RN
GAPDHS
DEK
ADAM19
TDG
PLCB4
HSD3B1
WHSC1
MRPL30
RBM6
MDK
PPP4R1
SUV420H1
HSPA8
hsa-mir-623
RNF44
AMPH
LILRB4
USP9X
BZW2
RBM28
EDEM3
MYO10
GLDC
ASAP1
CHRM2 QTRT1
hsa-mir-383
KLHL6
VAT1
CDK6
ITPR1
KIAA1598
ATXN1
UBL3
hsa-let-7f
STOM
VDR
THADA
ZBTB44
KLHDC10
ARHGEF2
IPMK
YLPM1IFNG
STT3B
CTDSPL2
WDR82UBR1
hsa-mir-517a
QSER1
BRF1
SLC39A10
RALY
GRSF1
FN1
NCOR2
CDK12
GNAQ
ZNF654
GALC
UBE2D1
FFAR2
H2AFZ
TOM1
NCKAP1
GSRhsa-mir-220b
SERF2
GNG2RAF1
RGS19
SETD1A
IMPA2FGF18
ERAP1
STAG1
IKZF4
ELMOD2
PROS1
PLB1
STAMBPL1
ERC1
STXBP4PPARG
hsa-mir-154
ACVR2A
NEU1
TMTC2RPL18
IL9RPAP2 DCBLD2
IL19
HEPHL1
MID2
GIMAP8
CAMK2D
MYH9
TERF2DHRS7
KIFC1 hsa-mir-380
CYP1A1
ADH5
SRRT
EPN1
GNG4
PTPN4
PER1
ABCB8
DHX9
RGS3
hsa-mir-545
SLC48A1
PCGF2
RGS1
TXK
ADARB1
ASB13
AURKB
MPZL1
OVGP1
RHOH
TPD52
hsa-mir-143
RPS23
hsa-mir-99b
CNOT10
RAB11B
ATRNL1
USP15
WDFY2
RAB9A
hsa-mir-571
NAV2
ZRSR2
PHOX2A
ATXN7L2
RABGGTA
FGFR1OP2
NCLN
RAB8A
TTLL4
FOXJ3
ZMIZ2
ETS2
LAG3
EGLN3
ZNF318
UXT
CLDND1
TTBK1
CYCS
CRTAM
hsa-mir-30b
NDUFA10
hsa-mir-505
TMEM159
hsa-mir-379
SMC5
RNF19A
GPRASP1
GABARAP
hsa-mir-24
EIF3K
SLC17A6
RPL19
CMPK1
TMEM30A
GRN
HIC1
TNFSF8
hsa-mir-30c
NBN
IL10
MAPK8
ADORA2B
CSNK1G3
MYO1E
LRP12
COIL
hsa-mir-603
FUT8
PPFIA1
hsa-mir-155
RPS6KA1
KPNA1
SIDT2
TRIM25
FDFT1
UBE2F
CTLA4
KIF3A
TARS2
hsa-mir-760
CCR3
LXN
KHDRBS1
MECR
PLCD1
HIVEP2
GLB1
RPL13A
GRIPAP1
MTOR
hsa-mir-148b
CHD7
DMXL1
TMOD4
INADL
NFX1
hsa-mir-422a
SH2D1A
DAG1
DOCK11
CLSTN1
INTS4
GZF1
RPL9
PARP4
RPS15A
SKP1
PLP2
ATP10A
hsa-mir-452
TAF8
HIPK1
MSH3
KLRG1
PDE2A
MLLT10
hsa-mir-181c
CCDC90B
SETX
CEP55
CNIH2
FRS2
FBXL5
MARS2
WDR33
U2AF2
TANK
PTPN12
ZBTB32
SPN
SLC25A27
IPCEF1
EP400
RGS13
hsa-mir-206ZNF592
PEX10
CWC15
hsa-mir-220c
PARD6G
DUSP4SUFU
JPH1
SH3PXD2A
S100A1
SLC44A1
SERPINF1
CYFIP1
USP28
FOXF2PRKAR2A VAMP4
GTF3C2 IL1R2
BAZ1Ahsa-mir-181a
MOCOS
FOXN2
ABCC1
ETV5
PAN3 RBM41
STK24
SETD7
SPHK2
MLF1
RTN3
ANXA2
SLC7A8
EZR
RC3H1
hsa-mir-548a-3p
RPE
PCGF5
LPXN
TREX1 LYN
MED13
RBM38
MKL2
RPS9
MAP3K5hsa-mir-620
MCL1
CD5PTPN6
ARL14MAPKAPK5 VCL
LCORL
CSNK1E
SUV39H1
NEB
RPL10
hsa-mir-497
PTPRJ
APLP1
HIST1H3B
DDX11
ARL6
DVL1 RPL13
RNPC3
ARL5A
DDX28
RPSAZMYM4
hsa-mir-487a
ZBTB38
DICER1
PTGER2
ARHGEF3
hsa-mir-520h
THRAP3
IL1R1
ABL2
NDUFA3
IL23R
LAT
HIPK2
hsa-mir-103
ARFGEF1
ZRANB1
KIAA2026
PTPN13
hsa-mir-29b
CRMP1
APBB3
IPO9
hsa-mir-488
ARHGEF12
SPP1
RPLP2
LLGL1
EIF3F
LZTFL1
FAM91A1
RPS5
DDX19B
PLXNC1
GIT1
TPBG
UHRF2
INHBA
hsa-mir-211
S100A11
ACTN1
ACVR1
HIST1H4C
CNTNAP4
GYPC
POLR3F
GMNN
TAOK2
ARG1
THOC1
KIF16B
PHF20
TIPARP
PPM1F
PTP4A3
SFN
OLR1
SIN3A
BCL2L11
RAPH1
ICOS
hsa-mir-374b IKBKE
SAP130
AHR
TOP2A
GNPTAB
PJA1
FGL2
ADAM9
EFR3A
SETBP1
HN1L
NUMA1
STUB1
SLK
GAD1
SERTAD2
GJB3
PALLD
C19orf66
GRM5
SLCO3A1
PIK3CB
ESYT2
KAT2A
FBXW11hsa-mir-520f
SUSD2
CNOT2
PAIP2
TRAM1 NCAPH
ANKRD13C
S100A16
HIRA
AQR
NR3C1
hsa-mir-222
CA8
ZDHHC3
CAPG
MTMR2
hsa-mir-551b
ZBTB10
MKNK1
PTPRC
ADAP1
RHOQ
PTPLAD1
CA5B
DAXX
UGP2
EBAG9
IKBKB
RAB11FIP4
IPPK
PRDX6
CYP11A1
RXRB
ZNF25
RNF146
ITGAD
RAB11FIP1
RNF216
PLAUR
CAP2
CYP17A1
TEX2
PLK2
TIMP3
SMAD1
TIMP1
RASSF8
DUSP14
RASA3
IGF2R
MKRN2
ITGB2
DDX46
TRA2A ZBTB11
CRY2
RPS28
PPAP2B
STARD13
RNF31
GLUD1
SESN3
CDK11APRICKLE3
CMAS LEF1
PHACTR4
MFHAS1
SMAD3OSBPL3
MUS81
CASP6
MYCBP2
MYLIP
USP34
hsa-let-7g
PSIP1
MAPKAPK2
NUP153
CASP4
TRPS1
QRICH1
KIF2C
PRKCH
SVIL
AXL
CTCF
CD2AP
EYA1
SRSF2
RPTOR
hsa-mir-214
ITGA2DUSP16
INTS7
MPI
MARK3
SMARCD3BNC1
PARP8
ACBD5
RNF111
HBS1L
ICAM2
MTMR10
GNPAT
RCOR1
hsa-mir-18a
PHC1
SRXN1
CCR1
B4GALT5
CAPNS1
MAPK8IP3
MSC
CDC27
GPR126
IER3
QPCTL
GATM
HSF1
MFNG
hsa-mir-93PKD2
GATA3
TOR1AIP2
KLRD1
GRAMD3
RRAS2
CASP1
KBTBD2
CREBL2
CABIN1
RASL11A
IGBP1
SPRED2HIST1H3C
AMMECR1
hsa-mir-554
RYK
MIA3
COX7A2L
CABYR
PPFIBP1EPS15L1
MAX
hsa-mir-182
TEC
RPS7
STK38
LGALS3
LRRFIP1
RPL30
BMPR2
CD80
PTDSS1
LGALS1
TNKS2
FARP2
SLC24A3
MRAS
PTPN9
SDC4
TSC2
CAPN2
PIK3AP1
CD44
CYTH3
NEK6MED26
ERCC4
FAM57A
TAF4B
HEG1
UTRN
BCAS2
ARHGAP29
GAPDH
HLF
DPM1hsa-mir-181b
NEO1
LTN1 ZC3H12C
hsa-mir-25
hsa-mir-152PDCD10
IL5 RBPJ
RALGDS
FOXP4
hsa-mir-610
CBLB
BCAR3
PDSS1
NDUFB1
ATR
ENY2
GIGYF2
PICK1
KIF13A
POGZ
AKAP6
hsa-mir-326
ATP10B
IL13
ITGB1BP1
hsa-mir-192
PRPF38B
SERPINA3
IL2RB
CLCN5
hsa-mir-195
IRF3
SSFA2
VASP
CDK4
ENAH
TADA2B
MAP3K14
hsa-mir-649
TRIM35
PJA2
NCAPG2
PBRM1
EPN2
SMC4REV1
GPR45
PACSIN1
PUM1
hsa-mir-874
SFRP4
MAP2K7
TMEM132B
hsa-mir-26a
DIAPH3
CD200R1
CDO1
BCL9
F2R
HS3ST3B1
hsa-mir-106a
FOXP3
NT5E
LSRATG4D
ARHGEF7
hsa-mir-23a
CEP68
TXNDC17
CHRAC1MLXIP
LCOR IFI16
ADCY3
JAG1
RAB31
PLCL1
RALGPS2
NPY
SUOX
SF3B4IGFBP7
CD27
hsa-mir-921
NDOR1
PHF3
RPL26hsa-mir-29a
XPO7
TAF7
TOP3A
CSTA
IMPDH1
hsa-mir-378
TAGAP
PCID2
NUP107
DGCR2
FBXL12
SOCS2
CSF1
CENPO
CRY1
CST3
KSR1
FGF2CNKSR3
MMP25
hsa-mir-32
UGCG
GTF2I
CLTA
SUCLG2
hsa-mir-625
IRF2BP1
FKBP3
hsa-mir-608
MAST1 SEMA4D
DCUN1D4
ERO1L
ZCCHC7
ST6GAL1
NDUFV2
GPM6B
MRPS24
STRBPRBM12
ZYG11B
RFK
CXCL10
hsa-mir-23bUBE2H
SLC35C1
TAGLN
APBB1IPMVP
GPATCH8
HLA-A
hsa-mir-132
NCK1
KIAA0922
POLD2
VPS54
CYP1B1
PIP4K2A
IRAK3
IKZF2
PANK4
hsa-mir-19b
STX1A
hsa-mir-425
ADPGK
UCHL3
AMBRA1
ID2
RPS29
ENTPD1
STAB1
hsa-mir-765
PLOD2FBXO4
TAF1D
UBP1
PRMT8
TSC22D4
hsa-mir-26b
PLCXD2
PRPF39
GAS2
PTPN5
MYNN
ATXN7L3
LTB4R
HS2ST1
ME1
NFIL3
DET1
hsa-mir-29c
NPNT
BTLA
HSPB1
R3HDM1
hsa-mir-448
GOT1
DHRS3
RAD50
SELL
ZFAND3
MAF
FLOT1
ETV6
DSTN
SCN1B
HOOK1
PDLIM5CHKA
CDKN2C
LYRM2
hsa-mir-636
MED1
hsa-mir-504
SLC4A2
hsa-mir-204
LGMN
ORMDL3
DOCK6
FNDC3A
KDSR
RAB20
ANGEL1
EML2
GNA15
IL22
NYNRIN
DIS3
hsa-mir-181d
TAF4
TBX21
MED18 GSN
RAB39B
FURIN
FES
hsa-mir-98
TASP1
CPNE1
SETDB2 CTDP1
ATG16L1
hsa-mir-30e
MPHOSPH9ZNF644
FAU
hsa-mir-216aTLE4
hsa-mir-27b
C2orf42
NDUFB6PEX13
ALDOA
RPL12
KLK3
KRTAP11-1
ELK3
DYRK1B
hsa-mir-936
SP1
EIF5
KLF12
PTK2Bhsa-mir-637
EDN1
IL17RBDCAF6
KCNK6
ANKRD12
ZNF281
PRR5L
CUL4B
ERGIC1
ZPBP2
PPP1R2 DENND5AKHSRP
TNFRSF1ACA13
ADAMTS6
HIP1
TMEM43
TRRAP
TSC22D1
XAB2
CASZ1
hsa-mir-593
hsa-mir-22
HSF2
SENP2
ATXN7L1
PTPN1
ACIN1
BTG2
STXBP1
hsa-mir-657
USP7
HAVCR2
IL12RB1
BOLL
RRM2
PICALM
CDK2
hsa-mir-20b
CD9
hsa-mir-449b RBBP9
PDSS2RASL11B
AGTPBP1 MBP
N4BP2 hsa-mir-372
GTF2A1
CCL19
MPRIP
GCNT1
SLC20A1
RNF138
N4BP1
SLC11A1
hsa-mir-766
DMXL2
NLRP3
ELL2LIN9
SMAD2
EZH2
DLG4
MYD88
ATP1B1GLRX
hsa-mir-7
DNAJC5B
WBP11DGKD
hsa-mir-301a
hsa-mir-363SF3B1
ARSB
FNTB
RNH1
hsa-mir-146a
BRWD1
CBFA2T2
SFPQIL6ST
IL10RA
ANGPTL2
B3GALT2
USP22
IL1RL1
RAB3GAP1
USE1
ID3
CBX7
CENPJ
DGKZ
PSME2
IFITM2
hsa-mir-302b
VRK2
PRDM1
CLCF1
NCKAP5
GOLGA1
ARL6IP6
hsa-mir-30a
PLCD4
TYMS
OGDH
STAT3
FBXW2
S100A6
ELK4
TIAM1
SDC1
hsa-mir-34a
RPL35A
FEZ2 PRSS8
SEC11A
NRF1
TWISTNB
FNBP4
EXOSC5
CRIM1
KLC1
C1GALT1
PKN2
HIST1H3HSUB1
VPS37A
LPHN2
KIF2A
AHDC1
PFKL
EMP1
ST6GALNAC2
TUBA4A
CD74
BACH2
ING2
FUNDC2
SRGAP3
MGRN1
EXOC6
TRAK2
DTYMK
PCSK1
SCGB1A1
CPD
RIOK3
CUL4A
CD38
hsa-mir-27a
PTBP2
CCR5
RNF128
hsa-mir-891b
GTF3A
CD79A
CTSH
NCOA3
FYN
CNOT6
IKZF3
PITPNM2
NKIRAS1
BCL3
NFS1
VAV1CLK2
ATXN2
SMG1

BUILDING THE TH2 DISEASE MODULE
Maximize Largest Connected Component (LCC)
size compare to random (Z-score)
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1.0 1.5 2.0 2.5
012345
Choosing a FC cutoff for mRNAs
Fold−change cutoff
LCCZscore
Optimal FC=1.3
stable
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1.0 1.5
01234
Choosing a
Fold
LCCZscore
O
F

MIRNAS IMPACT
• LCC Z score after removal of miRNA targets
• Compare to random targets
Th2 stableTh2 early
123456
e(r)
cute
c(r)
onic
Assessing the collective attack of miRNA
LCCZscore
miRNAs impact on
Th2 disease module
random
observed

COLLECTIVE IMPACT
Th2 stable
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Ranking
by FC
Ranking
by LCC
impact
mir-27b
mir-206
mir-106b
mir-203
mir-23b
High
Collective
Impact
●
1 2
−0.50.00.51.01.52.02.5
Nu
●
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●
Compare observed impact with random expectation (Z-score)

Target candidates for screeningTARGET CANDIDATES FOR SCREENING

EFFECT OF MIRNA ON TARGET EXPRESSION

MUTATING PREDICTED TARGET BINDING SITE

Nucleofection of Th2 cells followed by ﬂow cytometry
PHENOTYPE VALIDATION

II. TAKE HOME MESSAGE
Marc Santolini - CCNR
IDEAL

PART III
PERSONALIZED RESPONSE TO A
PERTURBATION
Marc Santolini, Milagros C. Romay, Clara L. Yukhtman, Christoph D. Rau, Shuxun Ren, Jeffrey J.
Saucerman, Jessica J. Wang, James N. Weiss, Yibin Wang, Aldons J. Lusis, Alain Karma, “A
Personalized, Multi-Omics Approach Identiﬁes Genes Involved In Cardiac Hypertrophy And
Heart Failure”

Hybrid Mouse Diversity Panel
100+ strains of mice
DNA variants (500k+ SNPs)
Heart Failure HMDP: Overview of the Study
Isoproterenol
Induced
Phenotypic
Spectrum
Compensated
Heart
Decompensated
Heart
Hybrid Mouse Diversity Panel
Treat with 20 mg/kg body
weight/day ISO for 3
weeks
Collected phenotypes
Weights
Heart (total and
each chamber)
Liver Lung
Adrenal Body
Echocardiography
Ejection Fraction
& Fractional
Shortening
LV chamber
diameter
(diastolic and
systolic)
Posterior Wall
Thickness (diastolic
and systolic)
E/A Ratio LV mass Ejection Time
Other
Plasma Lipids Fibrosis
% Death in
Response to
Isoproternol
Mitochondrial
DNA content
RNA Transcriptomes
THE HYBRID MOUSE DIVERSITY PANEL (HMDP)
Ghazalpour et al, Mamm Genome (2012)
Combines genetic and phenotypic diversity
Spectrum of phenotypes
42 traits pre/post ISO
mRNAs microarrays
+ISO
Isoproterenol-induced
heart failure (3 weeks)
List of collected phenotypes
Weights
Heart (total and each chamber) Body%
Adrenal Liver Lung
Echocardiography
Ejection
Fraction
& Fractional
Shortening
LV chamber
diameter
(diastolic and
systolic)
Posterior Wall
Thickness
(diastolic and
systolic)
E/A Ratio LV mass Ejection Time
Other
Myocyte Cross-
section Area
Fibrosis Apoptosis
Mitochondrial
DNA content
RNA Transcriptomes

GLOBAL VS PERSONALIZED RESPONSES
Spectrum of responses
Heart mass
h
Fold-Change
PHENOTYPE LEVEL
BXD−14/TyJ
BXA16/PgnJ
DBA/2J
BXA24/PgnJ
BXD32/TyJ
BXD−32/TyJ
BXHB2
KK/HlJ
AXB19/PgnJ
CXB−3/ByJ
AXB−20/PgnJ
BXD75
BUB/BnJ
LG/J
NOD/LtJ
BXD−11/TyJ
FVB/NJ
PL/J
CXB−12/HiAJ
BXA−4/PgnJ
BXD66
BXD62
SJL/J
CXBH
BXD50
BXH−19/TyJ
BXD87
RIIIS/J
SEA/GnJ
129X1/SvJ
BXD56
SM/J
BXH−9/TyJ
BXD48
BALB/cByJ
BXA−2/PgnJ
C3H/HeJ
CBA/J
BXD45
BXD−38/TyJ
CXB−7/ByJ
AXB−6/PgnJ
BXD49
BALB/cJ
BXD73
BXD40/TyJ
BXD−40/TyJ
BXD43
AKR/J
CXB−6/ByJ
BXA14/PgnJ
C57BLKS/J
BXD64
BXD61
AXB10/PgnJ
BXA−8/PgnJ
BXD21/TyJ
BXA−1/PgnJ
NZB/BlNJ
BXD68
NON/LtJ
SWR/J
BXD84
BXD74
BXD71
BXD79
BXD−5/TyJ
BXA−7/PgnJ
BXA−11/PgnJ
BXD55
BXD−12/TyJ
BXD85
AXB18
BXD44
CE/J
AXB8/PgnJ
NZW/LacJ
BXD−24/TyJ
BXD70
BXD39/TyJ
LP/J
CXB−11/HiAJ
BXH−6/TyJ
CXB−13/HiAJ
1.01.31.6
Heart mass
Global / Uncorrelated
Serpina3n
FC = 3.9
r = -0.06
i
Fold-Change(log2)
GENE LEVEL CORRELATIONS
j
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−2 0 2 4
1.01.21.41.6
r = −0.058 (p=0.6)
Expression FC
TotalHeartFCHeartmassFC
−1012345
Serpina3n
Individual / Correlated
Kcnip2
FC = 0.85
r = 0.41
k
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−2 −1 0 1 2
1.01.21.41.6
r = 0.41 (p=0.00011)
Expression FC
TotalHeartFC
Expression FC (log2)
−1.5−0.50.51.5
Fold-Change(log2)
HeartmassFC
l
Kcnip2

●
SAM
FC
0.00.10.20.30.4
CorrelationwithHypertrophy(abs.)
AXB10/PgnJ_ISO
BXD39/TyJ_ISO
BXH−19/TyJ_ISO
BXD−20/TyJ_ISO
BXA16/PgnJ_ISO
BXA14/PgnJ_ISO
BUB/BnJ_ISO
−3 0 2
Fold−Change (log2)
Fold-Change (log2)
-3 0 -3
HeartmassFC
Bclaf1
AW549877
Scara5
Mkrn3
Corin
Akap9
Zfp523
Ttc13
Klhl23
Pcdhgc4
Rffl
Cab39l
Ppp1r9a
Tnni2
Tspan17
9430041O17Rik
Ankrd1
Kcnip2
Fgf16
Kremen
Prnpip1
Ptrf
Ehd2
Nppb
Lrrc1
Dtr
Hes1
Atp6v0a1
Gss
4930504E06Rik
Nipsnap3b
Dedd
Nfatc1
2310022B05Rik
Wdr1
Eif4a1
AXB18_ISO
PL/J_ISO.1
BXD−5/TyJ_ISO
BXA−8/PgnJ_ISO
129X1/SvJ_ISO
129X1/SvJ.1_ISO
NZB/BlNJ_ISO
BXD70_ISO
BXD44_ISO
BXA−7/PgnJ_ISO
C57BL/6J_ISO.1
C57BL/6J.1_ISO.1
CXB−3/ByJ_ISO
BXH−6/TyJ_ISO
PL/J_ISO
CE/J_ISO
BXD49_ISO
BXD85_ISO
BXD−40/TyJ_ISO
BXD62_ISO
BXD73_ISO
BXD68_ISO
BALB/cJ_ISO
BXD−12/TyJ_ISO
CXB−13/HiAJ_ISO
AXB19/PgnJ_ISO
RIIIS/J_ISO
SWR/J_ISO
LP/J_ISO
NON/LtJ_ISO
CBA/J_ISO
BXA−11/PgnJ_ISO
BXD45_ISO
BXD84_ISO
BXD55_ISO
BXD61_ISO
CBA/J.1_ISO
SM/J_ISO
NZW/LacJ_ISO
DBA/2J.1_ISO
BXD−24/TyJ_ISO
BXD74_ISO
BXD64_ISO
BXA−2/PgnJ_ISO
BXD79_ISO
LG/J_ISO
BALB/cByJ_ISO
BXD−38/TyJ_ISO
C57BLKS/J_ISO
BXD87_ISO
BXD48_ISO
AXB8/PgnJ_ISO
C3H/HeJ_ISO
BXD50_ISO
BXD43_ISO
KK/HlJ_ISO
BXD71_ISO
BXD75_ISO
BXD56_ISO
AKR/J_ISO
FVB/NJ_ISO
BXD66_ISO
BXD40/TyJ_ISO
NOD/LtJ_ISO
NOD/LtJ_ISO.1
DBA/2J.1_ISO.1
DBA/2J_ISO.1
FVB/NJ_ISO.1
BXA−4/PgnJ_ISO
SJL/J_ISO
DBA/2J_ISO
BXD21/TyJ_ISO
AXB−20/PgnJ_ISO
BXD−14/TyJ_ISO
A/J_ISO.1
CXB−12/HiAJ_ISO
CXB−6/ByJ_ISO
CXB−11/HiAJ_ISO
BXH−19/TyJ_ISO
BXHB2_ISO
BXD−32/TyJ_ISO
BUB/BnJ.1_ISO
BXD39/TyJ_ISO
AXB10/PgnJ_ISO
BXD−20/TyJ_ISO
CXB−7/ByJ_ISO
BXA24/PgnJ_ISO
SEA/GnJ_ISO
BXD32/TyJ_ISO
BXA16/PgnJ_ISO
BXA14/PgnJ_ISO
BUB/BnJ_ISO
CXBH_ISO
BXA−1/PgnJ_ISO
AXB−6/PgnJ_ISO
BXD−11/TyJ_ISO
BXH−9/TyJ_ISO
FC genes
Correlation with Hypertrophy (abs.)
Frequency
0.0 0.1 0.2 0.3 0.4 0.5
01234567
Observed
Randomized
HYPERTROPHIC GENES IDENTIFICATION
a b
c d
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−2 −1 0 1 2
1.01.21.41.6
r = 0.41 (p=0.00011)
Expression FC
TotalHeartFC
36 best FC
genes selected
GLOBAL VS PERSONALIZED RESPONSES

DISEASE GENES ENRICHMENT
GWAS genes from HuGE database (2,747 diseases)
MOST ENRICHED DISEASE GENES SETS
HeartValveDiseases
TricuspidAtresia
Hypertension,Malignant
VentricularDysfunction
Hypertension,Renovascular
RenalArteryObstruction
Cardiomyopathy,Hypertrophic
HeartDiseases
Arthrogryposis
AorticValveStenosis
HeartArrest
HeartFailure,Systolic
PulmonaryValveStenosis
CardiovascularDiseases
Hypotension,Orthostatic
FC
SAM
Significant diseases for FC
p−value(−log10)
0.01.02.0
Cardiac Hypertrophy /
Heart Failure
NeuropsychologicalTests
CognitionDisorders
PsychologicalTests
PersonalityTests
AcuteDisease
Disease
JointDiseases
HeartDiseases
RheumaticDiseases
Taste
Fibrosis
Inflammation
Hypersensitivity
NasalPolyps
AtrialFibrillation
FC
SAM
Significant diseases for SAM
p−value(−log10)
0123
Fibrosis
(Remodeling)
e f

TWO DISTINCT CO-EXPRESSION MODULES
Ttc13
Kcnip2
Ankrd1
Cab39l
Prnpip1
Rffl
AW549877
Fgf16
Ehd2
2310022B05Rik
Bclaf1
Ptrf
Wdr1
Gss
Nipsnap3b
Kremen
Polydom
Acot1
Pacrg
Col14a1
Scara5
BC020188
Tnc
Timp1
Arpc3 Ms4a7
Clec4n Lox
DctTnc Arhgdig
Catnal1
Mfap5 Clecsf8
2610028H24Rik
Slc1a2Ces3
Fhl1
Fhl1
Panx1
Adamts2
Snai3
Adamts2
Nppb
Klhl23
Tnni2
9430041O17Rik
Dtr
Pcdhgc4
Eif4a1
Akap9
Ppp1r9a
Zfp523
Nfatc1
Hes1
4930504E06Rik
Atp6v0a1
Dedd
BC020188
Timp1
AI593442
Serpina3n
Cysltr1
Lgals3
Retnla
Rpp25Gp38
Corin
Lrrc1
BC025833
Mkrn3
Tspan17
CO-EXPRESSION NETWORKS
Tspan17
Fgf16
Wdr1
Kcnip2
Klhl23
Ehd2
Arpc3
Panx1
Timp1
Acot1
Arhgdig
Snai3
Lox
Rpp25
Serpina3n
BC025833
Rffl
Akap9
Prnpip1
AW549877
Cab39l
Atp6v0a1
Ttc13
4930504E06Rik
Hes1
Bclaf1
Dedd
9430041O17Rik
Nipsnap3b
Pcdhgc4
2310022B05Rik
Nfatc1Lrrc1
Zfp523
Lgals3
Timp1
Mfap5
Fhl1
Retnla
Adamts2
BC020188
Slc1a2
Gp38
AI593442
Ms4a7
Clecsf8
Adamts2
Clec4n
BC020188
Tnc
Polydom
Col14a1
Tnc
Nppb
Ppp1r9a
Corin Ptrf
Gss
Kremen
Cysltr1
Eif4a1
Ankrd1
Dtr
Tnni2
Fhl1
Dct
2610028H24Rik
Catnal1
Pacrg
Ces3
Scara5
FC
SAM
pre-ISO
post-ISO
Healthy ISO
FC module
SAM module
Inter modules
EdgedensityZscore
−50510
TWO DENSE, DISJOINT MODULES
a b
PREDICTED UPSTREAM TFs FOR FC GENES
1. Snai3
(#3 SAM gene)
2. Vdr
3. Gabpa 4. Srebf1
5. Mxd4 6. Hes1
(#10 FC gene)
c

PATHWAY ENRICHMENT OF NEIGHBORS IN THE INTERACTOME
8,693 pathways from mSigDb & wikipathways
d
INTERACTION WITH THE CARDIAC HYPERTROPHIC SIGNALING NETWORK (CHSN)
e
Proportion of PPI neighbors in CHSN
Expectedfrequency
0.00 0.05 0.10 0.15
050100150200
FC
Z = 4
SAM
Z = 1.2
FC gene

HES1 AS A CANDIDATE
−1.00.00.51.0
Fold-Change
BXD−14/TyJ
BXA16/PgnJ
DBA/2J
BXA24/PgnJ
BXD32/TyJ
BXD−32/TyJ
BXHB2
KK/HlJ
AXB19/PgnJ
CXB−3/ByJ
AXB−20/PgnJ
BXD75
BUB/BnJ
LG/J
NOD/LtJ
BXD−11/TyJ
FVB/NJ
PL/J
CXB−12/HiAJ
BXA−4/PgnJ
BXD66
BXD62
SJL/J
CXBH
BXD50
BXH−19/TyJ
BXD87
RIIIS/J
SEA/GnJ
129X1/SvJ
BXD56
SM/J
BXH−9/TyJ
BXD48
BALB/cByJ
BXA−2/PgnJ
C3H/HeJ
CBA/J
BXD45
BXD−38/TyJ
CXB−7/ByJ
AXB−6/PgnJ
BXD49
BALB/cJ
BXD73
BXD40/TyJ
BXD−40/TyJ
BXD43
AKR/J
CXB−6/ByJ
BXA14/PgnJ
C57BLKS/J
BXD64
BXD61
AXB10/PgnJ
BXA−8/PgnJ
BXD21/TyJ
BXA−1/PgnJ
NZB/BlNJ
BXD68
NON/LtJ
SWR/J
BXD84
BXD74
BXD71
BXD79
BXD−5/TyJ
BXA−7/PgnJ
BXA−11/PgnJ
BXD55
BXD−12/TyJ
BXD85
AXB18
BXD44
CE/J
AXB8/PgnJ
NZW/LacJ
BXD−24/TyJ
BXD70
BXD39/TyJ
LP/J
CXB−11/HiAJ
BXH−6/TyJ
CXB−13/HiAJ
1.01.31.6
Fold-Change(log2)
Hes1
Heart mass
Hes1 downregulation
Mild hypertrophy
Hes1 upregulation
Strong hypertrophy
a
b
Hes1 expression FC in the HMDP
Hes1 is a FC gene, a predicted TF and interacts with cardiac hypertrophy pathway

HES1 VALIDATION
a
VALIDATION OF HES1 AS A NEW CARDIAC HYPERTROPHY REGULATOR
b Effect on known hypertrophic markersHes1 expression 48h after
siRNA transfection
c Effect on cardiac
cell size
0
0.2
0.4
0.6
0.8
1
1.2
TransfectionControl
Hes1siRNA-1
Hes1siRNA-2
TransfectionControl
Hes1siRNA-1
Hes1siRNA-2
TransfectionControl
Hes1siRNA-1
Hes1siRNA-2
Control Isoproterenol Phenylepherine
FoldChangeRelativetoControl
0
0.5
1
1.5
2
2.5
Control
Isoproterenol
Phenylepherine
Control
Isoproterenol
Phenylepherine
Control
Isoproterenol
Phenylepherine
Transfection
Control
Hes1 siRNA 1 Hes1 siRNA 2
Fold Change Relative To Control
***
***
***
***
0
2
4
6
8
10
12
14
Control
Isoproterenol
Phenylepherine
Control
Isoproterenol
Phenylepherine
Control
Isoproterenol
Phenylepherine
Transfection
Control
Hes1 siRNA - 1 Hes1 siRNA - 2
Fold Change Relative To Control
***
***
***
***
Nppa
0
2
4
6
8
10
12
14
16
Control
Isoproterenol
Phenylepherine
Control
Isoproterenol
Phenylepherine
Control
Isoproterenol
Phenylepherine
Transfection
Control
Hes1 siRNA - 1 Hes1 siRNA - 2FoldChangeRelativetoControl
*
***
***
***
Nppb

III. TAKE HOME MESSAGE
−1012345
−1.5−0.50.51.5
a
b
INTERACTION WITH THE CARDIAC HYPERTROPHIC SIGNALING NETWORK (CHSN)
FC gene
predicted TF
Proportion of PPI neighbors in cardiac hypertrophic pathway
Expectedfrequency
0.00 0.05 0.10 0.15
050100150
FC
Z = 4.8
SAM
Z = −0.21
Proportion of PPI neighbors in CHSN
BXD−14/TyJ
BXA16/PgnJ
DBA/2J
BXA24/PgnJ
BXD32/TyJ
BXD−32/TyJ
BXHB2
KK/HlJ
AXB19/PgnJ
CXB−3/ByJ
AXB−20/PgnJ
BXD75
BUB/BnJ
LG/J
NOD/LtJ
BXD−11/TyJ
FVB/NJ
PL/J
CXB−12/HiAJ
BXA−4/PgnJ
BXD66
BXD62
SJL/J
CXBH
BXD50
BXH−19/TyJ
BXD87
RIIIS/J
SEA/GnJ
129X1/SvJ
BXD56
SM/J
BXH−9/TyJ
BXD48
BALB/cByJ
BXA−2/PgnJ
C3H/HeJ
CBA/J
BXD45
BXD−38/TyJ
CXB−7/ByJ
AXB−6/PgnJ
BXD49
BALB/cJ
BXD73
BXD40/TyJ
BXD−40/TyJ
BXD43
AKR/J
CXB−6/ByJ
BXA14/PgnJ
C57BLKS/J
BXD64
BXD61
AXB10/PgnJ
BXA−8/PgnJ
BXD21/TyJ
BXA−1/PgnJ
NZB/BlNJ
BXD68
NON/LtJ
SWR/J
BXD84
BXD74
BXD71
BXD79
BXD−5/TyJ
BXA−7/PgnJ
BXA−11/PgnJ
BXD55
BXD−12/TyJ
BXD85
AXB18
BXD44
CE/J
AXB8/PgnJ
NZW/LacJ
BXD−24/TyJ
BXD70
BXD39/TyJ
LP/J
CXB−11/HiAJ
BXH−6/TyJ
CXB−13/HiAJ
1.01.31.6
FC SAM
hypertrophy spectrum
Ttc13
Kcnip2
Ankrd1
Cab39l
Prnpip1
Rffl
AW549877
Fgf16
Ehd2
2310022B05Rik
Bclaf1
Ptrf
Wdr1
Gss
Nipsnap3b
Kremen
Polydom
Acot1
Pacrg
Col14a1
Scara5
BC020188
Tnc
Timp1
Arpc3 Ms4a7
Clec4n Lox
DctTnc Arhgdig
Catnal1
Mfap5 Clecsf8
2610028H24Rik
Slc1a2Ces3
Fhl1
Fhl1
Panx1
Adamts2
Snai3
Adamts2
Nppb
Klhl23
Tnni2
9430041O17Rik
Dtr
Pcdhgc4
Eif4a1
Akap9
Ppp1r9a
Zfp523
Nfatc1
Hes1
4930504E06Rik
Atp6v0a1
Dedd
BC020188
Timp1
AI593442
Serpina3n
Cysltr1
Lgals3
Retnla
Rpp25Gp38
Corin
Lrrc1
BC025833
Mkrn3
Tspan17
Global response

Method also used to ﬁnd genes associated to drug
response and classify responders vs non-responders

THANKS!
−1.00.00.51.0
Fold-Change
BXD−14/TyJ
BXA16/PgnJ
DBA/2J
BXA24/PgnJ
BXD32/TyJ
BXD−32/TyJ
BXHB2
KK/HlJ
AXB19/PgnJ
CXB−3/ByJ
AXB−20/PgnJ
BXD75
BUB/BnJ
LG/J
NOD/LtJ
BXD−11/TyJ
FVB/NJ
PL/J
CXB−12/HiAJ
BXA−4/PgnJ
BXD66
BXD62
SJL/J
CXBH
BXD50
BXH−19/TyJ
BXD87
RIIIS/J
SEA/GnJ
129X1/SvJ
BXD56
SM/J
BXH−9/TyJ
BXD48
BALB/cByJ
BXA−2/PgnJ
C3H/HeJ
CBA/J
BXD45
BXD−38/TyJ
CXB−7/ByJ
AXB−6/PgnJ
BXD49
BALB/cJ
BXD73
BXD40/TyJ
BXD−40/TyJ
BXD43
AKR/J
CXB−6/ByJ
BXA14/PgnJ
C57BLKS/J
BXD64
BXD61
AXB10/PgnJ
BXA−8/PgnJ
BXD21/TyJ
BXA−1/PgnJ
NZB/BlNJ
BXD68
NON/LtJ
SWR/J
BXD84
BXD74
BXD71
BXD79
BXD−5/TyJ
BXA−7/PgnJ
BXA−11/PgnJ
BXD55
BXD−12/TyJ
BXD85
AXB18
BXD44
CE/J
AXB8/PgnJ
NZW/LacJ
BXD−24/TyJ
BXD70
BXD39/TyJ
LP/J
CXB−11/HiAJ
BXH−6/TyJ
CXB−13/HiAJ
1.01.31.6
Fold-Change(log2)
Hes1
Heart mass
Hes1 downregulation
Mild hypertrophy
Hes1 upregulation
Strong hypertrophy
I. Topology vs dynamics
in perturbation
spreading
II. Network
perturbation by
miRNAs
III. Personalized
response to a
perturbation

PERTURBATION PATTERNS
Perturbation patterns can be exactly computed from the Jacobian matrix
(Barzel, Barabasi, Nat Biotech 2013)
S = (1 J) 1
D(
1
(1 J) 1
)
Perturbation patterns
(long distance correlations)
Jacobian
(direct inﬂuence)
Sij =
dxi/xi
dxj/xj
Jij =
@xi/xi
@xj/xj
(Vanunu et al, PLoS Comp Biol 2009)
The requirements on F can be expressed in linea
follows:
F~aW’Fz(1{a)YuF~(I{aW’){1
(1{a)Y
where W’ is a jVj|jVj matrix whose values are given b
F and Y are viewed here as vectors of size jVj. We re
eigenvalues of W’ to be in ½{1,1Š. Since a[(0,1), the ei
of (I{aW’) are positive and, hence, (I{aW’){1
exist
While the above linear system can be solved exactly
networks an iterative propagation-based algorithm works
is guaranteed to converge to the system’s solution. Speci
use the algorithm of Zhou et al. [21] which at iteration t
Ft
: ~aW’Ft{1
z(1{a)Y
where F1
: ~Y. This iterative algorithm can be best und
simulating a process where nodes for which prior informa
PLoS Computational Biology | www.ploscompbiol.or
weighted adjacency
matrix (normalized by
degrees of end-points)
prioritization
function
origin of impact
PRINCE algorithm
Network model
Biochemical model

Perturbing The Interactome: Multi-Omics And Personalized Methods For Network Medicine

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