Upload
others
View
3
Download
0
Embed Size (px)
Citation preview
Conserved cross-species network modules elucidate Th17 T-cell
differentiation in human and mouse
Mohammed El-Kebir, Hayssam Soueidan, Thomas Hume, Daniela Beisser, Marcus Dittrich, Tobias Müller, Guillaume Blin, Jaap Heringa, Macha Nikolski, Lodewyk F. A. Wessels, Gunnar W. Klau
ECCB 14 BioNetVisA Workshop
1“Trouble with mice is you always kill 'em. ” ― John Steinbeck, Of Mice and Men
T Cells
Wikimedia commons Abbas, Cellular & Molecular Immunology, 7th ed
2
t
215FUNCTIONAL RESPONSES OF T LYMPHOCYTES
expansion of the respective subsets (described later). In addition, these subsets of T cells differ in the expression of adhesion molecules and receptors for chemokines and other cytokines, which are involved in the migration of distinct subsets to different tissues (see Chapter 10).
Development of TH1, TH2, and TH17 SubsetsDifferentiated TH1, TH2, and TH17 cells all develop from naive CD4+ T lymphocytes, mainly in response to cyto-kines present early during immune responses, and dif-ferentiation involves transcriptional activation and epigenetic modification of cytokine genes. The process of differentiation, which is sometimes referred to as polar-ization of T cells, can be divided into induction, stable commitment, and amplification (Fig. 9-14). Cytokines act on antigen-stimulated T cells to induce the transcription of cytokine genes that are characteristic of differentiation toward each subset. With continued activation, epigen-etic changes occur so that the genes encoding that sub-set’s cytokines are more accessible for activation, and genes that encode cytokines not produced by that subset are rendered inaccessible. Because of these changes, the differentiating T cell becomes progressively committed to one specific pathway. Cytokines produced by any given subset promote the development of this subset and inhibit differentiation toward other CD4+ subpopula-tions. Thus, positive and negative feedback loops contrib-ute to the generation of an increasingly polarized population of effector cells.
There are several important general features of T cell subset differentiation.
! The cytokines that drive the development of CD4+ T cell subsets are produced by APCs (primarily dendritic cells and macrophages) and other immune cells (such as NK cells and basophils or mast cells) present at the site of the immune response. Dendritic cells that encounter
FIGURE 9–13 Properties of TH1, TH2, and TH17 subsets of CD4+ helper T cells. Naive CD4+ T cells may differentiate into distinct subsets of effector cells in response to antigen, costimulators, and cytokines. The columns to the right list the major differences between the best-defined subsets.
Signaturecytokines
Role indiseases
IFNγ
IL-4IL-5IL-13
IL-17AIL-17FIL-22
Immunereactions
Macrophageactivation;
IgG production
Mast cell,eosinophilactivation;
IgE production;"alternative"macrophage
activation
Neutrophilic,monocytic
inflammation
Hostdefense
Intracellularmicrobes
Helminthicparasites
Extracellularbacteria;
fungi
Autoimmunediseases;
tissue damageassociated with
chronic infections
Allergicdiseases
Organ-specificautoimmunity
TH1cell
TH2cell
TH17cell
consists of IgE antibody production and the activation of eosinophils. Along the same lines, in many chronic auto-immune diseases, tissue damage is caused by inflamma-tion with accumulation of neutrophils, macrophages, and T cells, whereas in allergic disorders, the lesions contain abundant eosinophils along with other leukocytes. The realization that all these phenotypically diverse immuno-logic reactions are dependent on CD4+ T cells raised an obvious question: How can the same CD4+ cells elicit such different responses? The answer, as we now know, is that CD4+ T cells consist of subsets of effector cells that produce distinct sets of cytokines, elicit quite different reactions, and are involved in host defense against differ-ent microbes as well as in distinct types of immunologic diseases. The first subsets that were discovered were called TH1 and TH2 (so named because they were the first two subsets identified). It was subsequently found that some inflammatory diseases that were thought to be caused by TH1-mediated reactions were clearly not dependent on this type of T cell, and this realization led to the discovery of TH17 cells (called TH17 because their characteristic cytokine is IL-17). In the next section, we describe the properties of these subsets and how they develop from naive T cells. We will return to their cyto-kine products, effector functions, and roles in cell-mediated immunity in Chapter 10.
The defining characteristics of differentiated subsets of effector cells are the cytokines they produce, the transcrip-tion factors they express, and epigenetic changes in cyto-kine gene loci. These characteristics of TH1, TH2, and TH17 cells are described below.
The signature cytokines produced by the major CD4+ T cell subsets are IFN-γ for TH1 cells; IL-4, IL-5, and IL-13 for TH2 cells; and IL-17 and IL-22 for TH17 cells (see Fig. 9-13). The cytokines produced by these T cell subsets determine their effector functions and roles in diseases. The cytokines also participate in the development and
Abbas, Cellular & Molecular Immunology, 7th ed
T Helper subsets
3
Differentiation
?
Naive Th ! cell
Th17 cells: Clinical relevance
• Mucosal immunology :Th17 respond to bacterial and fungal antigens!
• Th17 cells imbalance associated with several autoimmune diseases (Rheumatoid Arthritis, MS, psoriasis, lupus, CD) !
• IL-17-deficient mice are more susceptible to the development of lung melanoma!
• HIV infection specifically depletes Th17 population
4
Main questions
• How to modulate Th17 response to self?!
• What are the regulators of Th17 balance?!
• What are the proteins and pathways responsible for proper differentiation of Th17 cells?
How well findings in mouse are transferrable to human immunology?
5
Our strategy
• Let’s combine:!
• Human and Mouse Th17 differentiation transcriptomics data!
• Human and Mouse PPI networks!
• Orthology information between Human and Mouse!
• Using an optimization framework !
• To identify conserved cross-species active modules
+ +
Human Mouse
6
A B
C
DE
F
J
K
L
CCSAM:(1) Activity
7
A B
C
DE
F
J
K
L
CCSAM:(1I) Modularity
8
A B
C
DE
F
ab
c
d e
f
g
h
J
K
L
CCSAM:(1I) Modularity^2
9
A B
C
DE
F
ab
c
d e
f
g
h
J
K
L
CCSAM:(III) Conservation
10
Today
• Material: Gene expression profiling & public datasets!
• Method: Conserved active module !
• Results: Modules identification!
• Regulation at 2h and 72h are well conserved !
• Overall dynamics is conserved!
• Conclusions & future work
11
Today
• Material: Gene expression profiling & public datasets!
• Method: Conserved active module !
• Results: Modules identification!
• Regulation at 2h and 72h are well conserved !
• Overall dynamics is conserved!
• Conclusions & future work
12
M&M recipe:
• For each species individually:!
• Process RNA-Seq => Count matrix!
• Fit a GLM => estimated coefs!
• For each time point, !
• For each gene:!
• call for DE => p.value!
• Fit a BUM => activity score !
• Get PPI network & orthology relations
13
0 6 12 24 48 72
Timepoint (h)
Th0
Th17
CD4+umbilicalcordblood
TGF , IL6, I 1 , anti-IL4, anti-IFN�� �L
anti-IL4, anti-IFN�
BA
C
Th0 Th17
3 % 55 %
FSC
Th0 Th17
250
50
100
150
200
p < 0.05
RORC
0
20
40
60
80
100
120
140
160
180
24h 48h 72h
Th0Th17
D
p < 0.001
IL17F
0
20
40
60
80
100
120
24h 48h 72h
Th0Th17p < 0.05
p < 0.001
p < 0.001
Figure 1
0.5; 1; 2; 4
IL17A
Transcriptional profiling of Human & Mouse Th17 cells
Tuomela et al., 2014
• Control Th0 vs Th17!
• 9 time points, RNA-Seq!
• Human: 14,338 mRNAs quantified in the first 72h!
• Mouse: 11,751 mRNAs quantified in the first 72h!
• Matched time points!
• DE called with an edgeR GLM 14
Transcription dynamics
-1000
-500
0
500
1000
0.5 1 2 4 6 12 24 48 72tp
-N
posLogFC
FALSE
TRUE
Human DE genes, FDR<=0.1, |logFC|>=1
-1000
-500
0
500
1000
0.5 1 2 4 6 12 24 48 72tp
-N
posLogFC
FALSE
TRUE
Mouse DE genes, FDR<=0.1, |logFC|>=1
• Biphasic in both species!
• Seems “stronger” in the mouse samples!
•earliest changes (1/2h!) visible 15
AC026803.14+NUCB1 CNP CTD−2154I11.2+RHOBTB3 DTNBP1
FAM71B+HAVCR2+ITK+MED7 FNIP1+RAPGEF6 IFI16 IL6ST
KDSR TAF4B VAV1 ZNRF1
2000
4000
6000
8000
1000
2000
3000
100
200
300
400
500
250
500
750
1000
1250
10000
20000
30000
40000
0
10000
20000
0
5000
10000
15000
0
5000
10000
15000
0
1000
2000
3000
4000
0
1000
2000
3000
4000
0
3000
6000
9000
12000
0
500
1000
1500
0.5 1.0 2.0 4.06.0 12.0 24.0 48.072.0 0.5 1.0 2.0 4.06.0 12.0 24.0 48.072.0 0.5 1.0 2.0 4.06.0 12.0 24.0 48.072.0 0.5 1.0 2.0 4.06.0 12.0 24.0 48.072.0
0.5 1.0 2.0 4.06.0 12.0 24.0 48.072.0 0.5 1.0 2.0 4.06.0 12.0 24.0 48.072.0 0.5 1.0 2.0 4.06.0 12.0 24.0 48.072.0 0.5 1.0 2.0 4.06.0 12.0 24.0 48.072.0
0.5 1.0 2.0 4.06.0 12.0 24.0 48.072.0 0.5 1.0 2.0 4.06.0 12.0 24.0 48.072.0 0.5 1.0 2.0 4.06.0 12.0 24.0 48.072.0 0.5 1.0 2.0 4.06.0 12.0 24.0 48.072.0time
counts
is.th17
FALSE
TRUE
Contrasting: Genes DE at time 2
16
signal⇠ B(a,1)
noise⇠ uniform(0,1)⌘ B(1,1)
p−values (second order statistics)
Den
sity
0.0 0.2 0.4 0.6 0.8 1.0
02
46
810
1214
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
quantiles of expected p−values under the mixture model
obse
rved
p−v
alue
s (s
econ
d or
der s
tatis
tics)
signal + noisenoise
p-value
dens
ity
Scores: BUM Model
• Activity (positive and negative) scores derived from !
• p-values distribution!
• described with a beta-uniform mixture model;!
• controlling the FDR!
• using an adj. LL ratio:
s(x,FDR) = log
ax
a�1
a⌧(FDR)
a�1
=(a� 1) (log(x)� log(⌧(FDR)))
17
Estimating the occurrence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-values. Pounds 2003
Today
• Material: Gene expression profiling & public datasets!
• Method: Conserved active module !
• Results: Modules identification!
• Regulation at 2h and 72h are well conserved !
• Overall dynamics is conserved!
• Conclusions & future work
18
PPI Networks
• Obtained from the STRING db!
• Only kept physical interactions!
• Mouse network: 12,121 nodes and 176,462 edges !
• Human network : 14,280 nodes and 197,649 edges
19
Orthology relations
• Obtained from ENSEMBL orthology!
• Represented as a bi-partite graph M !
• 85,640 human proteins!
• 49,717 mouse proteins !
• linked by 125,685 edges!
• grouped in 16,736 bicliques (avg size of 8.08, median of 6, SD of 5.97)
A
B
C
D
E
a
b
c
d
e
20
M&M recipe (recap)
• For each species individually:!
• Process RNA-Seq => Count matrix!
• Fit a GLM => estimated coefs!
• For each time point, !
• For each gene:!
• call for DE => p.value!
• Fit a BUM => activity score !
• Get PPI network & orthology relations
21
Today
• Material: Gene expression profiling & public datasets!
• Method: Conserved active module !
• Results: Modules identification!
• Regulation at 2h and 72h are well conserved !
• Overall dynamics is conserved!
• Conclusions & future work
22
Conserved active modules
• Formalized using a constraint modeling approach over boolean variables!
• Constraints are linearized => MILP!
• The MILP is then solved using CPLEX with a branch-and-cut algorithm
A B
C
DE
F
ab
c
d e
f
g
h
J
K
L
23
MILP:?
• A formulation, with:!
• an objective function!
• subject to linear constraints!
• where variables can be constrained to discrete domains ({0,1}, )!
• much harder than on !
• for which exact solutions can be found efficiently in practice
max
x1,x2
S1 · x1 + S2 · x2
Subject to: x1 + x2 L
F1 · x1 + F2 · x2 F
P1 · x1 + P2 · x2 P
x1 � 0, x2 � 0
N
R
24
Boolean variables for nodes in solution
A B
C
DE
F
ab
c
d e
f
g
h
J
K
L
xA
xK
MILP:Variables
25
Weighted boolean variables for nodes in solution, objective function:
A B
C
DE
F
ab
c
d e
f
g
h
J
K
L
wAxA
wKxK
MILP:Variables
max
X
v2V1[V2
wvxv
26
Boolean variables for conserved nodes
A B
C
DE
F
ab
c
d e
f
g
h
J
K
L
wAxA
mA ma
mBmb
wKxK
MILP:Variables
mu = max
uv2M{xuxv}
27
constrained by having e.g more than of nodes being conserved
A B
C
DE
F
ab
c
d e
f
g
h
J
K
L
wAxA
mA ma
mBmb
wKxK
MILP: degree of conservation
⇠P
mvPxv
� 50%
↵ = 50%
28
• And satisfying the connectivity constraint:!• Possibly an exponential number of constraints!• Constraints added as needed during optimization
A B
C
DE
F
ab
c
d e
f
g
h
J
K
L
wAxA
mA ma
mBmb
wKxK
MILP: connectivity
29
Conserved cross-species network modules
G2 = (V2, E2). The nodes in these networks are labeled by theiractivity—defined by w 2 RV1[V2 . Conserved node pairs are givenby the mapping M ✓ V1 ⇥ V2.
PROBLEM 1 (Conserved active modules). Given G1, G2, w andM , the task is to find a subset of nodes V ⇤
= V
⇤1 [V
⇤2 with V
⇤1 ✓ V1
and V
⇤2 ✓ V2 such that the following properties hold.
• Activity: The sumP
v2V ⇤ wv is maximal. Note that it is up tothe user to normalize the node weights as to prevent a bias inactivity score towards one species.
• Conservation: At least a certain fraction ↵ of the nodes in thesolution must be conserved, that is, |U⇤| � ↵ · |V ⇤| whereU
⇤:= {u 2 V
⇤ | 9v 2 V
⇤: uv 2 M}.
• Modularity: The induced subgraphs G1[V⇤1 ] and G2[V
⇤2 ] are
connected.
There is a trade-off between conservation and activity. If noconservation is enforced (↵ = 0), the solution will correspond totwo independent maximum-weight connected subgraphs therebyachieving maximal overall activity. Conversely, if completeconservation is required (↵ = 1), the solution can only consistof conserved nodes, which may result in smaller overall activity. Theuser controls this trade-off by varying the value of the parameter↵ from 0 to 1. The activity score monotonically decreases withincreasing ↵—see Fig. 2.
Since the maximum-weight connected subgraph problem, whichoccurs as a subproblem for ↵ = 0, is NP-hard (Ideker et al., 2002),the problem of finding conserved active modules is NP-hard as well.
2.2 Integer linear programming approachWe formulate the conserved active modules problem as an integerprogramming (IP) problem in the following way.
max
X
v2V1[V2
wvxv (1)
s.t. mu = max
uv2M{xuxv} u 2 V1 (2)
mv = max
uv2M{xuxv} v 2 V2 (3)
X
v2V1[V2
mv � ↵
X
v2V1[V2
xv (4)
G1[x] and G2[x] are connected (5)
xv,mv 2 {0, 1} v 2 V1 [ V2 (6)
In the following we show that the above formulation satisfies theproperties of activity, conservation and modularity. In addition wegive further details on the actual integer linear program (ILP).
Activity. Variables x 2 {0, 1}V1[V2 encode the presence of nodes inthe solution, i.e., for all v 2 V1 [ V2 we want xv = 1 if v 2 V
⇤ andxv = 0 otherwise. The objective function (1) uses these variables toexpress the activity of the solution, which we aim to maximize.
Conservation. Variables m 2 {0, 1}V1[V2 encode the presence ofconserved nodes in the solution. Recall that a node u 2 V
⇤1 (u 2 V
⇤2 )
that is present in the solution is conserved if there is another nodein the solution v 2 V
⇤2 (v 2 V
⇤1 ) such that the two nodes form
3
-1
3
4
3
-1
3
4
3
-1
3
4
3
-1
3
4
3
-1
3
4
3
-1
3
4
↵ = 0 ↵ = 0.5 ↵ = 1activity: 10 activity: 9 activity: 8
G1 G2 G1 G2 G1 G2
Fig. 2: Trade-off between activity and conservation. Threeoptimal solutions (indicated in yellow) for varying conservationratios ↵ in a toy example instance. Node activities are given nextto the nodes, conserved node pairs are linked by dotted lines. Theactivity of a conserved module is the sum of the activities of itscomprising nodes. The parameter ↵ denotes the minimum fractionof nodes in a solution that must be conserved, i.e. connected by adotted line.
a conserved node pair uv 2 M (vu 2 M ). This corresponds toconstraints (2) and (3). We linearize xuxv , in a standard way, byintroducing binary variables z 2 {0, 1}M such that zuv = xuxv forall uv 2 M :
zuv xu uv 2 M (7)
zuv xv uv 2 M (8)
zuv � xu + xv � 1 uv 2 M (9)
zuv 2 {0, 1} uv 2 M (10)
Subsequently, we model the max function in (2) and (3) as follows.
mu � zuv uv 2 M (11)
mv � zuv uv 2 M (12)
mu X
uv2M
zuv u 2 V1 (13)
mv X
uv2M
zuv v 2 V2 (14)
We model the required degree of conservation by constraint (4).
Modularity. Constraint (5) states that the nodes encoded in thesolution x induce a connected subgraph in both G1 and G2. Thereare many ways to model connectivity, e.g., using flows or cuts(Magnanti and Wolsey, 1995). Cut-based formulations perform betterin practice (Dilkina and Gomes, 2010). Recently, Alvarez-Mirandaet al. (2013) have introduced a cut-based formulation that only usesnode variables. In an empirical study, the authors show that theirformulation outperforms other cut-based formulations. We modelconnectivity along the same lines. Since the constraints that we willdescribe are similar for both graphs, we introduce them only forgraph G1 = (V1, E1).
X
v2V1
yv 1 (15)
3
MILP: Formulation
30
Today
• Material: Gene expression profiling & public datasets!
• Method: Conserved active module !
• Results: Modules identification!
• Regulation at 2h and 72h are well conserved !
• Overall dynamics is conserved!
• Conclusions & future work
31
CCND1
NTRK1
NR3C1
PTPN1
FURIN
VIM
APBB1IP
PTMS FSCN1
LMNB1
CTLA4
POU2F2
IGF2BP3
SFXN3
ARID5B
PFKFB3
LPP
ELAVL1
BCL3
STAT1
NOTCH1
IL2RB
CD274
SOCS3
TNF
IL23A
IER3
STAT3
ZEB2
CMPK1
PIM1BHLHE40
ETV6
CMPK2
C5AR1
MAPK11
NCK2
BATF
PIM2
DDR1
CEBPZ
DOK2
JAK3
CXCR5
LAG3BCL6
COL1A2
COL18A1
GZMB BATF3
JUNB
2h conserved module
Il2rb
Bcl3Vim
Stat3
Casp6
Socs3
Ctla4
Tbx21
Stat1
Il6stJak3
Apbb1ip
Fyn
Ablim1
Ptpn1
Bcl6
Nr3c1
I l21
Cxcr5
Hif1a
Pou2f2
Lmnb1
Prdm1
Hdac7
Junb
Batf
Cebpz
Lag3
Notch1
Gzmb
Lpp
Phf15
Hmox1
Vhl
Socs1
32
IL17F
CCR6
NCALD
IL17RA
LRRN3
ANXA1
CCL20
GPR132
CCR4
CCL5
CYSLTR1
KIF5A
KIF5C
CAMK2B
KIFC3
NCOA2
UCHL1
RYR1
RORC CTSL1COPS5
JUN
MAP3K5
SMAD3
FASLG
FOXP3
PPARG
SMAD7HIF1A
IL24
IL22AP1G1
RORA
IL23R
LMNA
IL12RB2
B2M
IL21
GPR65
KLRD1LTB4R
SOCS3
BCAR3
RUNX1
AHR
FNBP1L PMEPA1CYP1B1
VDRSKILGADD45G NEDD4
KIF26B
GATA3SYN1IL9
NEDD9
IL13
CHN1
FES
CDK5
ICAM1STAT3 IL2RB
CSF2
TGFBI
CD27
PLAUR
MRC2
KLF2 TNFRSF8
TNFRSF12A
FURIN TNFSF8
TRAF5
ITGB7
BASP1RBPJ
THBS1FOXO1
NOTCH1
IL1RNPTGER2PTHLH
IL1R1TUBB3PIK3R1
ATM
IL1A
ATP1B1PDE7B
DPP4
PHLDA1
ELAVL1
ZNF238NCS1
GNAQPFKFB3
PRDM1
SESN3
AQP3
SOS1TIMP1
LPL
MMP2TNF
PTK2
IRS2
GRB7 RDH10
COL15A1
RGS16
CHST3
COL6A3
TIAM2ITGAE
ITGA3JAG2
TNIK
72h conserved module
Dpp4
Itga4
Klrc1
Gap43
Plaur
Itgb7
Klrd1
Il12rb2
Procr
B2m
Rabep1
Furin
Basp1
Rdx
Pdpn
Ap1g1
Tnf
Lum
F3
Klf2
Icam1
Fasl
Rbpsuh-rs3
Lpxn
Camk2b
Ptk2Notch1
Uchl1
Klf3
Srf
Mt2Il17ra
Egln3
RorcIl17f
Il17a
Ltb4r1
Nedd4
Inhba
Cysltr1
Piwil2
Gpr132
Gpr65
Gnaq
Il9
Pik3r1
Cdk5 Syn1
Irs2Lmna
I l24
Il1r1
Epha2
Crem
AtmTgm2
Tec
Fes
Ccl20
Cxcl10Ccl5
Timp1I l22
Ly6aCcr4
Stat6
I l21Hif1a
Il23r
I l10
Map3k5
Csf2
Smad7
Il2rbJag2
Stat3Socs3
Cops5
Tnfrsf8
Bmp7
Cd27
Malt1
Tnfsf8 Ms4a4b
Gadd45gTraf5Rbpj
Ccr5
Cyp1b1
Maf
Ncoa2
Ahr Gata3
RoraKif5c
Kifc3
33
Today
• Material: Gene expression profiling & public datasets!
• Method: Conserved active module !
• Results: Modules identification!
• Regulation at 2h and 72h are well conserved !
• Overall dynamics is conserved!
• Conclusions & future work
34
2h 4h 24h 48h 72h
0
25
50
75
100
125
0
2
4
6
0
25
50
75
100
125
0
100
200
0
100
200
300
400
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0α
Size
Class Positive, conserved Positive Negative,conserved Negative
Distribution of node classes by alpha
Overall dynamics and solutions
●
●
●
●
●
●
●
●
●
● ●
● ●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
2h 4h 24h 48h 72h
50
100
150
0
2
4
6
0
50
100
150
0
200
400
0
200
400
600
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0α
Scor
e
Species ● ●Human Mouse To optimality ● FALSE TRUE
Module score by alpha
35
Today
• Material: Gene expression profiling & public datasets!
• Method: Conserved active module !
• Results: Modules identification!
• Regulation at 2h and 72h are well conserved !
• Overall dynamics is conserved!
• Conclusions & future work
36
Conclusions
• Mouse and Human Th17 differentiation processes are well conserved during the first 72h !
• Differentiation happens in two phases, very early (0h--4h) and late (12h--72h) !
• We provide the first formulation of the conserved active module problem as well as an efficient MILP solver !
• Code and recipes available there: http://software.cwi.nl/xheinz!
• PS: We got the same results on an independent data set! 37
Future work
• Theoretical results on the computational complexity for specific network topologies!
• Novel formulation for bi-conservation: Conservation between species and across time!
• Non-supervised formulation: Clustering of samples based on conserved active modules
38
Thanks!
• Wessels group / NKI!• CWI for the expertise!• CBIB/CGFB for the environment (and computing power!)!• VU Centre for Integrative Bioinformatics!• ERASysBio for the funding!• Riitta Laheesma’s group for the data!• And you for your attention... and for trying the tool:
http://software.cwi.nl/xheinz39
and see you @ poster 7!
IL10
IL10RA
IL12RB2
IL13IL16
IL17A
IL17F
IL17RAIL1A
IL1R1
IL1R2
IL2
IL21
IL22
IL23R
IL24
IL2RB
IL4
IL7R
IL9
0.0
2.5
5.0
7.5
10.0
0 5 10hsa
mus
72h: What are the DE IL?
40
●●●●●●
●●●●
●●●●●
●●●●●●
●●●●●●●
●●●●●●●●
●
●
●
●
●
●●●●
●
●
●
●
●
●
●●●●
●
●
●
●
●
●
●
●
●
●
● 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
2h
1
1
0.99
0.84
0.79
0.71
0.65
0.46
0.39
0.33
0.15
1
0.99
0.84
0.79
0.71
0.65
0.46
0.39
0.33
0.15
1
0.86
0.81
0.73
0.66
0.48
0.4
0.35
0.15
1
0.94
0.82
0.74
0.53
0.43
0.37
0.18
1
0.88
0.79
0.58
0.45
0.39
0.2
1
0.9
0.66
0.51
0.46
0.22
1
0.75
0.59
0.49
0.21
1
0.78
0.6
0.25
1
0.71
0.26
1
0.33 1
2h
●●●●●●
●●●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●●
●
●
●
●
●●●
●
●
●
●
●
●●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●● 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
4h
1
1
1
1
0.25
0.25
0.25
0.2
0.2
0
0
1
1
1
0.25
0.25
0.25
0.2
0.2
0
0
1
1
0.25
0.25
0.25
0.2
0.2
0
0
1
0.25
0.25
0.25
0.2
0.2
0
0
1
1
1
0.5
0.5
0
0
1
1
0.5
0.5
0
0
1
0.5
0.5
0
0
1
1
0
0
1
0
0
1
1 1
4h
●●●●●●
●●●●
●●●●●
●●●●●●
●●●●●●●
●●●●●●●●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
24h
1
0.95
0.78
0.69
0.63
0.52
0.49
0.48
0.34
0.18
0
1
0.74
0.73
0.63
0.52
0.49
0.48
0.32
0.17
0
1
0.85
0.69
0.58
0.55
0.53
0.34
0.17
0
1
0.76
0.64
0.57
0.54
0.32
0.16
0.02
1
0.84
0.7
0.66
0.38
0.19
0.02
1
0.78
0.73
0.44
0.25
0.02
1
0.88
0.52
0.25
0
1
0.58
0.28
0
1
0.46
0
1
0 1
24h
●●●●●●
●●●●
●●●●●
●●●●●●
●●●●●●●
●●●●●●●●
●
●
●●●●●●●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
● 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
48h
1
1
0.82
0.74
0.69
0.63
0.57
0.51
0.44
0.28
0.01
1
0.82
0.74
0.69
0.63
0.57
0.51
0.44
0.28
0.01
1
0.85
0.78
0.72
0.66
0.58
0.5
0.31
0.01
1
0.91
0.82
0.72
0.64
0.56
0.34
0.01
1
0.91
0.79
0.69
0.6
0.38
0.01
1
0.86
0.75
0.64
0.43
0.01
1
0.81
0.7
0.48
0.01
1
0.8
0.52
0.01
1
0.55
0.01
1
0.02 1
48h
●●●●●●
●●●●
●●●●●
●●●●●●
●●●●●●●
●
●●●●●●●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
72h
1
0.89
0.79
0.73
0.68
0.61
0.53
0.45
0.34
0.24
0
1
0.88
0.81
0.76
0.64
0.55
0.49
0.33
0.25
0
1
0.84
0.79
0.67
0.57
0.51
0.36
0.25
0
1
0.91
0.75
0.65
0.56
0.39
0.3
0
1
0.82
0.69
0.61
0.4
0.31
0
1
0.76
0.65
0.45
0.35
0
1
0.8
0.51
0.4
0
1
0.6
0.43
0
1
0.51
0.01
1
0.01 1
72h
Influence of conservation on content
J(A,B) =|A \B||A [B|
41
3
-1
3
4
3
-1
3
4
3
-1
3
4
3
-1
3
4
3
-1
3
4
3
-1
3
4
↵ = 0 ↵ = 0.5 ↵ = 1activity: 10 activity: 9 activity: 8
G1 G2 G1 G2 G1 G2
Controls tradeoff between overall activity and conservation
MILP: Conservation tradeoff
42
ATP1B1 CHST2 CTSL1 DIXDC1
ITGAE NOTCH1 PFKFB3 PMEPA1
PPP1R16B RYR1 SKIL STAP1
0
500
1000
1500
0
2000
4000
6000
0
4000
8000
12000
010002000300040005000
0
1000
2000
3000
01000020000300004000050000
0
2500
5000
7500
10000
0
1000
2000
3000
0
1000
2000
3000
4000
0
2500
5000
7500
10000
0
2500
5000
7500
0
200
400
600
800
0.5 1.0 2.0 4.06.0 12.0 24.0 48.072.0 0.5 1.0 2.0 4.06.0 12.0 24.0 48.072.0 0.5 1.0 2.0 4.06.0 12.0 24.0 48.072.0 0.5 1.0 2.0 4.06.0 12.0 24.0 48.072.0
0.5 1.0 2.0 4.06.0 12.0 24.0 48.072.0 0.5 1.0 2.0 4.06.0 12.0 24.0 48.072.0 0.5 1.0 2.0 4.06.0 12.0 24.0 48.072.0 0.5 1.0 2.0 4.06.0 12.0 24.0 48.072.0
0.5 1.0 2.0 4.06.0 12.0 24.0 48.072.0 0.5 1.0 2.0 4.06.0 12.0 24.0 48.072.0 0.5 1.0 2.0 4.06.0 12.0 24.0 48.072.0 0.5 1.0 2.0 4.06.0 12.0 24.0 48.072.0time
coun
ts
is.th17
FALSE
TRUE
Genes affected at any time
Genes affected at any time point
43
T cells response 205SIGNALS FOR T LYMPHOCYTE ACTIVATION
by the T cells themselves and by APCs and other cells at the site of antigen recognition. In this section, we will summarize the nature of antigens recognized by T cells and discuss specific costimulators and their receptors that contribute to T cell activation. Cytokines are discussed later in the chapter.
Recognition of Antigen
Antigen is always the necessary first signal for the activa-tion of lymphocytes, ensuring that the resultant immune response remains specific for the antigen. Because CD4+ and CD8+ T lymphocytes recognize peptide-MHC com-plexes displayed by APCs, they can respond only to protein antigens or chemicals attached to proteins. In addition to the TCR recognizing peptides displayed by MHC molecules, several other T cell surface proteins par-ticipate in the process of T cell activation (see Fig. 7-9, Chapter 7). These include adhesion molecules, which stabilize the interaction of the T cells with APCs, and costimulators, which are described later. The nature of the biochemical signals delivered by antigen receptors and the role of these signals in the functional responses of the T cells are discussed in Chapter 7.
for returning the immune system to a state of equilib-rium, or homeostasis. It occurs mainly because the majority of antigen-activated effector T cells die by apop-tosis. One reason for this is that as the antigen is elimi-nated, lymphocytes are deprived of survival stimuli that are normally provided by the antigen and by the costim-ulators and cytokines produced during inflammatory reactions to the antigen. It is estimated that more than 90% of the antigen-specific T cells that arise by clonal expansion die by apoptosis as the antigen is cleared.
With this overview, we proceed to a discussion of the signals required for T cell activation and the steps that are common to CD4+ and CD8+ T cells. We then describe effector and memory cells in the CD4+ and CD8+ lineages, with emphasis on subsets of CD4+ helper T cells and the cytokines they produce. We conclude with a discussion of the decline of immune responses.
SIGNALS FOR T LYMPHOCYTE ACTIVATION
The proliferation of T lymphocytes and their differentia-tion into effector and memory cells require antigen rec-ognition, costimulation, and cytokines that are produced
FIGURE 9–2 Phases of T cell responses. Antigen recognition by T cells induces cytokine (e.g., IL-2) secretion, particularly in CD4+ T cells, clonal expansion as a result of cell proliferation, and differentiation of the T cells into effector cells or memory cells. In the effector phase of the response, the effector CD4+ T cells respond to antigen by producing cytokines that have several actions, such as the recruitment and activation of leukocytes and activation of B lymphocytes, and CD8+ CTLs respond by killing other cells.
APC
Naive CD4+
T cell EffectorCD4+
T cell
EffectorCD8+ T cell(CTL)
MemoryCD8+
T cell
Activation ofmacrophages,
B cells,other cells;
inflammation
Killing ofinfected
"target cells";macrophage
activation
MemoryCD4+
T cell
Naive CD8+
T cell
Cytokines(e.g., IL-2)
IL-2R
Lymphoid organ Peripheral tissue
Antigenrecognition
Lymphocyteactivation
Clonalexpansion
EffectorfunctionsDifferentiation
Abbas, Cellular & Molecular Immunology, 7th ed 44
Experimental design
RNA-sequencing
!"#$"%&'()"*'++&(",#
-&.&"/"-&.&"0"1"-&.&"*
""""02"""""""304""""1""""03
!"#$%&'()"*&+#,",-%#
!+.+)/+',)"#/0)-1,-%#
*567)"$%#*"'")'*+8& >%'?*&.,&("*567) @A67)
7"B&@,#%"#$"@#C.,)
0%2#,-#*
&"11-#*
DE5"
'*+8:$:@',:#."F"
)&GC&.@:.?
*'++&("%&'()7"8:),"#$"%&'()
$%#*"-&.&"/H
$%#*"-&.&"03
1
$%#*"-&.&"2I
7JJ-EE;;;
-EJ77E;;;
1
7-EEJE;;;
KK"K"KK
K"KK
1
KK"KK"K
KK"K"KK
K"KK
1
KK"KK"K
!!!!!
!!!
1
!!!!!
J. Aubert () Stat. challenges RNA-Seq ETEGE, 2012 April 26 10 / 65
RNA-Seq
45
Human exp. design:
Donor
Donor
Donor
Donor
Donor
Donor
Donor
72h48h24h12h6h4h2h1h0.5h
Th0
Th17
72h48h24h12h6h4h2h1h0.5h
Donor
Donor
Donor
Donor
Donor
Donor
Donor
72h48h24h12h6h4h2h1h0.5h
Th0
Th17
72h48h24h12h6h4h2h1h0.5h
Donor
Donor
Donor
Donor
Donor
Donor
Donor
72h48h24h12h6h4h2h1h0.5h
Th0
Th17
72h48h24h12h6h4h2h1h0.5h
46
−0.4 −0.2 0.0 0.2 0.4
−0.3
−0.2
−0.1
0.0
0.1
0.2
GLM MDS 0.5h
Dimension 1
Dim
ensi
on 2
Th0_0.5h_rep1
Th0_0.5h_rep2
Th0_0.5h_rep3
Th17_0.5h_rep1
Th17_0.5h_rep2
Th17_0.5h_rep3
Strongest effects: 0.5h
47
−0.4 −0.2 0.0 0.2 0.4
−0.2
−0.1
0.0
0.1
0.2
GLM MDS 12h
Dimension 1
Dim
ensi
on 2
Th0_12h_rep1
Th0_12h_rep2
Th0_12h_rep3
Th17_12h_rep1
Th17_12h_rep2
Th17_12h_rep3
Strongest effects: 12h
48
−0.6 −0.4 −0.2 0.0 0.2 0.4 0.6
−0.4
−0.3
−0.2
−0.1
0.0
0.1
0.2
GLM MDS 48h
Dimension 1
Dim
ensi
on 2
Th0_48h_rep1
Th0_48h_rep2
Th0_48h_rep3
Th17_48h_rep1Th17_48h_rep2
Th17_48h_rep3
Strongest effects: 48h
49
−1.0 −0.5 0.0 0.5
−0.6
−0.4
−0.2
0.0
0.2
0.4
Dimension 1
Dim
ensi
on 2
Th0_0.5h_rep1Th0_1h_rep1
Th0_2h_rep1
Th0_4h_rep1Th0_6h_rep1 Th0_12h_rep1
Th0_24h_rep1
Th0_48h_rep1
Th0_72h_rep1Th17_0.5h_rep1Th17_1h_rep1
Th17_2h_rep1
Th17_4h_rep1
Th17_6h_rep1Th17_12h_rep1
Th17_24h_rep1
Th17_48h_rep1Th17_72h_rep1
Th0_0.5h_rep2Th0_1h_rep2
Th0_2h_rep2
Th0_4h_rep2Th0_6h_rep2
Th0_12h_rep2
Th0_24h_rep2
Th0_48h_rep2
Th0_72h_rep2Th17_0.5h_rep2
Th17_1h_rep2
Th17_2h_rep2
Th17_4h_rep2Th17_6h_rep2
Th17_12h_rep2
Th17_24h_rep2
Th17_48h_rep2Th17_72h_rep2Th0_0.5h_rep3Th0_1h_rep3
Th0_2h_rep3
Th0_4h_rep3Th0_6h_rep3
Th0_12h_rep3
Th0_24h_rep3
Th0_48h_rep3
Th0_72h_rep3Th17_0.5h_rep3
Th17_1h_rep3
Th17_2h_rep3
Th17_4h_rep3Th17_6h_rep3
Th17_12h_rep3
Th17_24h_rep3
Th17_48h_rep3Th17_72h_rep3
Between all samples
50
−0.6
−0.4
−0.2
0.0
0.2
0.4
−0.5 0.0 0.5xMDS
yMD
S
rep.num
1
2
3
time
20
40
60
0.5
0.5
is.th17
FALSE
TRUE
MDS HSA all samples
0.5h
12h
4h 6h
24h
48h
72h
1h
2h
Between all samples
51
Chapter 9 – Activation of T Lymphocytes216
microbes and display microbial antigens are activated to produce cytokines (as well as costimulators, described earlier) as part of innate immune responses to the microbes (see Chapter 4). Different microbes may stimulate dendritic cells to produce distinct sets of cytokines, perhaps because the microbes are recog-nized by different microbial sensors in the cells. Other cells of innate immunity, such as NK cells and mast cells, also produce cytokines that influence the pattern of T cell subset development.
! Stimuli other than cytokines may also influence the pattern of helper T cell differentiation. Some studies indicate that different subsets of dendritic cells selec-tively promote either TH1 or TH2 differentiation; the same principle may be true for TH17 cells. In addition, the genetic makeup of the host is an important deter-minant of the pattern of T cell differentiation. Inbred mice of some strains develop TH2 responses to the same microbes that stimulate TH1 differentiation in most other strains. Strains of mice that develop TH2-dominant responses are susceptible to infections by intracellular microbes (see Chapter 15).
! The distinct cytokine profiles of differentiated cell pop-ulations are controlled by particular transcription factors that activate cytokine gene transcription and by chromatin modifications affecting cytokine gene loci. The transcription factors are themselves activated or induced by cytokines as well as by antigen receptor stimuli. Each subset expresses its own characteristic set of transcription factors. As the subsets become increas-ingly polarized, the gene loci encoding that subset’s signature cytokines undergo histone modifications (changes in methylation and acetylation) and conse-quent chromatin remodeling events, so that these loci are “accessible” and in an “open” chromatin configura-tion, whereas the loci for other cytokines (those not produced by that subset) are in an inaccessible chro-matin state. These epigenetic changes ensure that each subset can produce only its characteristic collection of cytokines. It is likely that epigenetic changes in cyto-kine gene loci correlate with stable phenotypes, and before these changes are established, the subsets may be plastic and convertible.
! Each subset of differentiated effector cells produces cytokines that promote its own development and may suppress the development of the other subsets. This feature of T cell subset development provides a power-ful amplification mechanism. For instance, IFN-γ secreted by TH1 cells promotes further TH1 differentia-tion and inhibits the generation of TH2 and TH17 cells. Similarly, IL-4 produced by TH2 cells promotes TH2
FIGURE 9–14 Development of TH1, TH2, and TH17 subsets. Cytokines produced early in the innate or adaptive immune response to microbes promote the differentiation of naive CD4+ T cells into TH1, TH2, or TH17 cells by activating transcription factors that stimulate production of the cytokines of each subset (the early induction step). Progressive activation leads to stable changes in the expressed genes (commitment), and cytokines promote the development of each population and sup-press the development of the other subsets (amplification). These prin-ciples apply to all three major subsets of CD4+ effector T cells.
Dendriticcell
Cytokinegene expression
Cytokines
Othercells
Cytokinesecretion
Transcriptionfactors
Cytokinereceptor
Naive T cell
Chromatinchanges stable cytokinegene expression
PolarizedTH subset
Inhibitionof other
TH subsets
Induction
Amplification
Commitment
Th cells development & differentiation
Abbas, Cellular & Molecular Immunology, 7th ed 52
219FUNCTIONAL RESPONSES OF T LYMPHOCYTES
(see Chapter 14), promotes the development of proin-flammatory TH17 cells when other mediators of inflam-mation, such as IL-6 or IL-1, are present. Some experimental results indicate that TGF-β does not directly stimulate TH17 development but is a potent suppressor of TH1 and TH2 differentiation and thus removes the inhibitory effect of these two subsets and allows the TH17 response to develop under the influence of IL-6 or IL-1. According to this idea, the action of TGF-β in promoting TH17 responses is indirect. TH17 cells produce IL-21, which may further enhance their development, provid-ing an amplification mechanism.
The development of TH17 cells is dependent on the transcription factors RORγ t and STAT3 (see Fig. 9-17). TGF-β and the inflammatory cytokines, mainly IL-6 and IL-1, work cooperatively to induce the production of RORγt, a transcription factor that is a member of the retinoic acid receptor family. RORγt is a T cell–restricted protein encoded by the RORC gene, so sometimes the protein may be referred to as RORc. The inflammatory cytokines, notably IL-6, activate the transcription factor STAT3, which functions with RORγt to drive the TH17 response. Mutations in the gene encoding STAT3 are the cause of a rare human immune deficiency disease called Job’s syndrome because patients present with multiple bacterial and fungal abscesses of the skin, resembling the biblical punishments visited on Job. These patients have defective TH17 responses.
TH17 cells appear to be especially abundant in mucosal tissues, particularly of the gastrointestinal tract, suggest-ing that the tissue environment influences the generation of this subset, perhaps by providing high local concentra-tions of TGF-β and other cytokines. This observation also suggests that TH17 cells may be especially important in combating intestinal infections and in the development of intestinal inflammation. The development of TH17 cells in the gastrointestinal tract is also dependent on the local microbial population.
The functions of differentiated effector cells of the CD4+ lineage are mediated by surface molecules, primar-ily CD40 ligand, and by secreted cytokines. We will describe the cytokines produced by differentiated CD4+ effector cells and their functions in Chapter 10.
Differentiation of CD8+ T Cells into Cytotoxic T Lymphocytes
The activation of naive CD8+ T cells requires antigen recognition and second signals, but the nature of the second signals may be different from those for CD4+ cells. We have previously described the role of dendritic cells in presenting antigens to and costimulating naive CD8+ cells.
The full activation of naive CD8+ T cells and their dif-ferentiation into functional CTLs and memory cells may require the participation of CD4+ helper cells. In other words, helper T cells can provide second signals for CD8+ T cells. The requirement for helper cells may vary accord-ing to the type of antigen exposure. In the setting of a strong innate immune response to a microbe, if APCs are directly infected by the microbe, or if cross-presentation
microbes, such as fungi, but also when cells infected with various bacteria and fungi undergo apoptosis and are ingested by dendritic cells. IL-23 may be more important for the proliferation and maintenance of TH17 cells than for their induction. TH17 differentiation is inhibited by IFN-γ and IL-4; therefore, strong TH1 and TH2 responses tend to suppress TH17 development. A surprising aspect of TH17 differentiation is that TGF-β, which is produced by many cell types and is an anti-inflammatory cytokine
FIGURE 9–17 Development of TH17 cells. IL-1 and IL-6 pro-duced by APCs and transforming growth factor-β (TGF-β) produced by various cells activate the transcription factors RORγt and STAT3, which stimulate the differentiation of naive CD4+ T cells to the TH17 subset. IL-23, which is also produced by APCs, especially in response to fungi, stabilizes the TH17 cells. TGF-β may promote TH17 responses indirectly by suppressing TH1 and TH2 cells, both of which inhibit TH17 differentia-tion (not shown in the figure). IL-21 produced by the TH17 cells amplifies this response.
Dendriticcell
Bacteria, fungi
Naive T cell
Sources?
IL-22IL-17
TGF-β
TGF-βIL-6
IL-6IL-1
IL-23
IL-21
Effector functions:InflammationBarrier function
RORγt
TH17 cells
STAT3Th17 cells development & differentiation
Abbas, Cellular & Molecular Immunology, 7th ed 53
Mice vs Men
• Cells origin? !
• Role of TGFB? !
• Secreted cytokines?
Kobezda et al. (2014). Of mice and men: how animal models advance our understanding of T-cell function in RA. Nat. Reviews. Rheumatology54
edgeR GLM
• We fit a model of the like :!
• Here: mean ~ donor + time + treat:time !
• Test for DE by contrasting <=> H0: treat:time ==0 !
• LRT, compare models counts ~ donor + time VS counts ~ donor + time + time:treat
MATERIALS AND METHODS
Biological coefficient of variation
RNA-Seq profiles are formed from n RNA samples. Letpgi be the fraction of all cDNA fragments in the i-thsample that originate from gene g. Let G denotethe total number of genes, so
PGg¼1 !gi ¼ 1 for each
sample. Letffiffiffi"
pg denote the coefficient of variation (CV)
(standard deviation divided by mean) of pgi between thereplicates i. We denote the total number of mapped readsin library i by Ni and the number that map to the g-th geneby ygi. Then
EðygiÞ ¼ #gi ¼ Ni!gi:
Assuming that the count ygi follows a Poisson distributionfor repeated sequencing runs of the same RNA sample, awell known formula for the variance of a mixture distri-bution implies:
varðygiÞ ¼ E! varðyj!Þ½ % þ var! Eðyj!Þ½ % ¼ #gi þ "g#2gi:
Dividing both sides by #2gi gives
CV2ðygiÞ ¼ 1=#gi þ "g:
The first term 1/mgi is the squared CV for the Poissondistribution and the second is the squared CV of the un-observed expression values. The total CV2 therefore is thetechnical CV2 with which pgi is measured plus the bio-logical CV2 of the true pgi. In this article, we call fg thedispersion and
ffiffiffiffiffi"g
pthe biological CV although, strictly
speaking, it captures all sources of the inter-library vari-ation between replicates, including perhaps contributionsfrom technical causes such as library preparation as wellas true biological variation between samples.
GLMs
GLMs are an extension of classical linear models tonon-normally distributed response data (42,43). GLMsspecify probability distributions according to theirmean–variance relationship, for example the quadraticmean–variance relationship specified above for readcounts. Assuming that an estimate is available for fg, sothe variance can be evaluated for any value of mgi, GLMtheory can be used to fit a log-linear model
log#gi ¼ xTi $g þ logNi
for each gene (32,41). Here xi is a vector of covariatesthat specifies the treatment conditions applied to RNAsample i, and bg is a vector of regression coefficients bywhich the covariate effects are mediated for gene g. Thequadratic variance function specifies the negative binomialGLM distributional family. The use of the negativebinomial distribution is equivalent to treating the pgi asgamma distributed.
Fitting the GLMs
The derivative of the log-likelihood with respect to thecoefficients bg is XTzg, where X is the design matrix withcolumns xi and zgi=(ygi' mgi)/(1+fgmgi). The Fisher
information matrix for the coefficients can be written asIg=XTWgX, where Wg is the diagonal matrix of workingweights from standard GLM theory (43). The Fisherscoring iteration to find the maximum likelihoodestimate of bg is therefore $new
g ¼ $oldg þ % with
d=(XTWgX)'1 XTzg. This iteration usually produces an
increase in the likelihood function, but the likelihood canalso decrease representing divergence from the requiredsolution. On the other hand, there always exists astepsize modifier a with 0< a< 1 such that$newg ¼ $old
g þ &% produces an increase in the likelihood.Choosing a so that this is so at each iteration is knownas a line search strategy (44,45).
Fisher’s scoring iteration can be viewed as an approxi-mate Newton-Raphson algorithm, with the Fisher infor-mation matrix approximating the second derivativematrix. The line search strategy may be used with anyapproximation to the second derivative matrix that ispositive definite. Our implemention uses a computationallyconvenient approximation. Without loss of generality, thelinear model can be parametrized so that XTX= I. If this isdone, and if the mgi also happen to be constant over i for agiven gene g, then the information matrix simpifies consid-erably to mg/(1+fgmg) times the identity matrix I. Takingthis as the approximation to the information matrix, theFisher scoring step with line search modification becomessimply d= aXTzg, where the multiplier mg/(1+fgmg) hasbeen absorbed into the stepsize factor a. In this formula-tion, a is no longer constrained to be less than one. In ourimplementation, each gene has its own stepsize a that isincreased or decreased as the iteration proceeds.
Cox–Reid adjusted profile likelihood
The adjusted profile likelihood (APL) for fg is thepenalized log-likelihood
APLgð"gÞ ¼ ‘ð"g; yg; $gÞ '1
2log det Ig:
where yg is the vector of counts for gene g, $g is theestimated coefficient vector, ‘() is the log-likelihoodfunction and Ig is the Fisher information matrix. TheCholesky decomposition (46) provides a numericallystable and efficient algorithm for computing the determin-ant of the information matrix. Specifically, logdet Ig is thesum of the logarithms of the diagonal elements of theCholesky factor R, where Ig=RT R and R is upper tri-angular. The matrix R can be obtained as a by product ofthe QR-decomposition used in standard linear modelfitting. In our implementation, the Cholesky calculationsare carried out in a vectorized fashion, computed for allgenes in parallel.
Simulations
Artificial data sets were generated with negative binomialdistributed counts for a fixed total number of 10 000 genes.The expected count size varied between genes according toa gamma distribution with shape parameter 0.5, an ad hocchoice that happened to mimic the size distribution of thecarcinoma data. The average dispersion was set to 0.16(BCV=0.4). In one simulation, all genes had the same
4290 Nucleic Acids Research, 2012, Vol. 40, No. 10
at Netherlands Cancer Institute on O
ctober 2, 2012http://nar.oxfordjournals.org/
Dow
nloaded from
55
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
● ●●● ● ●● ●● ●●● ● ● ●● ●● ●●● ●●
−2
0
2
4
−1 0 1 2 3Human logFC
Mou
se lo
gFC
FDR <= 0.05 ●● ●●FALSE TRUE
STAT3
BATF
SOCS
3
2h dynamics of conserved module
56
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ● ●●● ●● ●● ●● ●● ●● ●● ● ●●●● ● ●● ●● ●● ●● ● ● ●●● ●● ●● ● ●● ● ● ● ●●● ●●●● ●● ●● ●●● ● ●●● ●●
−5
0
5
10
0 5 10Human logFC
Mou
se lo
gFC
FDR <= 0.05 ●● ●●FALSE TRUE
72h dynamics of conserved module
IL9
RORC
IL17F
GATA3
STAT3
57
A
CB D E F
MILP: connectivity constraints
• For each connected component of the current solution S!
• Determine its neighborhood not in S!
• Formulate the two alternatives:!
• It’s expanded towards other CCs!
• Or it’ll be the final module (new variables)
xA xD+yA + yB + yC
xB xD+yA + yB + yC
xC xD+yA + yB + yC
xE xD+yE + yF
xF xD+yE + yF
with yv xv andX
v2V
yv 1;
yv
58