Duel of the fates - MRC Laboratory of Molecular Biology

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Duel of the fates: the role of transcriptional circuits and
noise in CD4+ cells
Daniel Hebenstreit1, Andrew Deonarine1,
M Madan Babu and Sarah A Teichmann
CD4+ T cells play key roles in orchestrating adaptive immune
responses, and are a popular model for mammalian cell
differentiation. While immune regulation would seem to require
exactly adjusted mRNA and protein expression levels of key
factors, there is little evidence that this is strictly the case.
Stochastic gene expression and plasticity of cell types contrast
the apparent need for precision. Recent work has provided
insight into the magnitude of molecular noise, as well as the
relationship between noise, transcriptional circuits and
epigenetic modifications in a variety of cell types. These
processes and their interplay will also govern gene expression
patterns in the different CD4+ cell types, and the determination
of their cellular fates.
Address
MRC Laboratory of Molecular Biology, Hills Road, Cambridge CB2 0QH,
UK
Corresponding author: Teichmann, Sarah A ([email protected])
These authors contributed equally to this work.
1
Current Opinion in Cell Biology 2012, 24:350–358
This review comes from a themed issue on
Nucleus and gene expression
Edited by Asifa Akhtar and Karla Neugebauer
Available online 11th April 2012
0955-0674/$ – see front matter
# 2012 Elsevier Ltd. All rights reserved.
DOI 10.1016/j.ceb.2012.03.007
Introduction
Cells change phenotype when they specialize or adapt to
different environments. There are several well-known
systems that illustrate this phenomenon. Some examples
include the recently reported reprogramming of differentiated cells into induced pluripotent stem cells (iPS) [1]
or the differentiation of CD4+ T cells [2]. These changes
or ‘switches’ in cellular phenotype are influenced by
changes in expression levels of key genes [3], and are
subject to molecular fluctuations [4]. Such noise can be
classified as either intrinsic or extrinsic [5–8]. Intrinsic
noise is caused by gene-specific stochastic fluctuations in
abundances of molecules that are due to transcription,
translation, and related biochemical mechanisms, while
extrinsic noise originates from variations in the state or
concentration of other components in the cell [5]. As a
result, abundances of mRNAs and proteins of identical
genes can vary widely from cell to cell, constituting
‘noise,’ even within a homogenous cell population.
Noise propagation is strongly influenced by topologies of
regulatory networks [9,10], such as the transcriptional
circuits controlling cell fate. Examples include small
subnetworks, such as feedforward loops (FFLs) [11]
and feedback loops which can buffer or amplify noise,
depending on the parameters that influence the kinetics
of gene expression [12]. Recent theoretical studies have
tried to explore the structural requirements of network
controllability [13,14]. This question is important in the
context of ‘master regulators,’ which are essentially small
numbers or even single transcription factors (TFs) that
are able to ‘reprogram’ a cell type [15]. Besides the basic
TF–target gene interactions, other factors such as miRNA
[16], and epigenetic modifications [17,18] are part of
broader regulatory circuits and contribute to stabilization
or switching of phenotypes.
In this review we will focus on CD4+ T cells, which is a
good biological system for illustrating the relationships
between transcriptional circuits, expression level noise,
and epigenetics. CD4+ T cells form an important branch
of the adaptive immune system and function mainly by
secreting certain cytokines that regulate the behavior of
other cells, thus orchestrating the immune response [2].
Six major CD4+ T cell subtypes have been well characterized to date: the naı̈ve, T-helper 1 (Th1), Th2 and
Th17 cells, and induced and natural regulatory T cells
(iTreg and nTreg). Most of the (‘mature’-) subtypes are
derived from naı̈ve CD4+ T cells, which enter a proliferation and differentiation process upon antigenic activation in the presence of cytokines (Figure 1a).
The phenotypic switches that CD4+ cells undergo upon
differentiation are linked to changes in epigenetic modifications [19]. These switches are largely driven by TFs,
and illustrate the importance of master regulators, such as
Gata3 [20,21]. Recent studies on Th2 cells suggest that
phenotypic switches are mirrored by transcriptomic
switches [22]. Protein expression levels of cytokines are
usually subject to large cell-to-cell variability, highlighting
the importance of noise in protein and mRNA expression.
To understand the interplay between these factors, several questions need to be addressed: how do transcriptional circuits mediate phenotypic switching? What are
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The role of transcriptional circuits and noise in CD4+ cells Hebenstreit et al. 351
Figure 1
(a)
Differentiation
Mature Cell ype
Master Regulator
Secreted Cytokines
Gata3
Tbx21
Rorγt
II4
II5
II13
Ifng
II17
100 150 200
100
(b)
H3K27me3
200
F = 4.00
µ = 0.7
0
0
50
H3K4me3
F = 83.9
µ = 54.9
400
600
0
800
10
20
30
40
250
500
200
100
200
300
bivalent
F = 10.6
µ = 0.9
0 100
0
50
H3K4me3
F = 38.3
µ = 35.6
0
Tbx21
300
150
II7r
Frequency
II2
600
Cd4
0
in Th2 cells
Foxp3
0
400
10
20
30
40
2500
60
mRNAs/cell
40
Gata3
Gata3 (Protein)
H3K4me3
1000
H3K4me3
20
F = 28.4
0
0
µ = 82.6
0
100
200
300
mRNAs/cell
400
0
500
1000 1500 2000 2500
3000
Fluorescence/cell (AU)
Current Opinion in Cell Biology
Noise in CD4+ cells. (a) The CD4+ T cell system. Mature CD4+ subtypes are derived from naı̈ve CD4+ cells through a differentiation process (except
nTreg, which mature inside the thymus). Master regulators and cytokines characteristic for the various subtypes are indicated. Plasticity is indicated by
arrows between the mature subtypes. (b) Skewed distributions of absolute mRNA numbers or protein expression (for Gata3 only, based on FACS
experiments) per single Th2 cells. Sample means (m) and Fano factors (F = s2/m) are given. The status of histone marks at genes was ascertained with
EpiChIP [52]. The data used were from Hebenstreit et al. [29] (mRNA and protein) and Wei et al. [22] (histone marks).
the roles of epigenetic modifications in this process? What
role does noise play: is it detrimental, tolerated, or even
beneficial? On the basis of the CD4+ T cell system, we
explore possible answers.
Phenotypic switches in CD4+ T cells are
mediated by transcriptional circuits
The difficulty of precisely defining cell types is well
illustrated by the distinct yet closely related subtypes
352 Nucleus and gene expression
of CD4+ T cells (Figure 1a). Though each of the six cell
types illustrated in Figure 1a is associated with a particular master regulator and defined by a specific cytokine
profile, there are many reports of intermediate cell types
as well as cell-type switching after differentiation (i.e.
plasticity), and reprogramming. For instance, mature Th2
cells can be induced to give rise to a mixed Th1/Th2
phenotype, Th17 cells can be transformed into Th1 cells,
or iTreg cells can be differentiated towards Th17 cells
[23] (Figure 1a).
The cytokines that are secreted by mature CD4+ T cells
have crucial, often opposing roles in regulating the immune
response. Thus, one would expect a tight regulation in
maintaining lineage identity. The plasticity in the differentiation process as a whole is due to the complexity of the
transcriptional regulatory circuits, which generally include
many more components than just a single master regulator.
In Figure 2b, a small transcriptional circuit involving the
master regulator Gata3 is illustrated. The circuit consists of
overlapping FFLs which facilitate the differentiation of a
CD4+ naı̈ve cell to the Th2 type [24], and represents a
small subnetwork in a much larger, more complex regulatory network involved in phenotypic switching and in
stabilizing the cellular state.
Differentiation of CD4+ cells is strongly linked to cellular
proliferation as many studies show a percentage increase
of cytokine-secreting T cells in successive cell generations [25,26]. Notably, the percentage of differentiated
cells added after each division is small and thus suggests
the involvement of a stochastic mechanism. Where does
this randomness come from and how can we reconcile it
with the precision of immune responses?
Sources of stochasticity and their relation to
circuit topology
T cell differentiation is regulated by TFs. It has become
clear in recent years that transcriptional regulatory networks are subject to intrinsic noise in the expression of
their components [27]. This has been shown by techniques that focus on single cells rather than population
averages, such as single molecule RNA-FISH, which
measures absolute numbers of mRNAs per cell [8].
One of the surprising findings from these assays was that
the distributions of mRNA numbers per cell are usually
non-Poissonian, meaning that the variances are higher
than the mean levels of transcripts. This gives some
mechanistic insight into transcription by RNA polymerase II (RNAPII) by hinting at a burst-like production of
transcripts, where a gene is transcribed multiple times in
short periods of time [28]. Our studies on Th2 cells [29]
confirm such highly skewed, ‘noisy’ distributions of transcript numbers per cell for five genes classically associated
with CD4+ cell biology, including Gata3 and CD4 itself
(Figure 1b). Skewed distributions are observed for
protein expression levels too (Figure 1b), suggesting that
either the mRNA expression noise is not buffered completely or downstream processes serve as additional
sources of noise. An interesting possibility is that cellto-cell variation in the expression levels of genes such as
Gata3 may simply reflect the heterogeneity in terms of
their recent signaling experience. Thus some genes could
be specifically programmed to be sensitive indicators of
different levels of signaling molecules in each cell.
Another contributor to the wide range of expression levels
between individuals in a cell population could be asymmetric cell division. Polarized segregation of proteins has
been observed upon antigen-induced T cell division,
where activation and differentiation induced factors were
preferentially found in the daughter cell close to the
immunological synapse [30,31]. This included Tbx21
and may also affect other TFs and master regulators.
As a consequence of these sources of noise, there may not
be a simple, discrete transcriptional ‘switch.’ This may
vary from cell to cell depending on the number of copies
of a set of signaling molecules, including TFs. As a result,
statistical parameters such as the average burst size or the
average waiting time may be altered upon transcriptional
activation of RNAPII. Importantly, burst-like transcription demonstrates that transcription is an intrinsically
noisy process per se, and in individual cells this phenomenon can result in large deviations in transcript numbers
from the population average. In principle, random fluctuations give rise to different transcription rates in different
cells over time, even if the cells started under identical
conditions. For CD4+ cells, the large variance in the
distributions shown in Figure 1b suggests that this may
be relevant to phenotypic switching, as it means that a
certain fraction of cells will have high numbers of mRNAs
from genes that are lowly expressed, and vice versa.
The presence of large cell-to-cell variation may indicate
that noise is somewhat inevitable. This might be due to
the fact that noise suppression is a biologically expensive
process: a recent study showed that suppression only
occurs with the fourth root of the number of control
factors in a transcriptional circuit [32]. However, many
investigations in other cell types or systems have shown
that stochastic transcriptional switches can even be
advantageous over deterministic ones. For instance, stochastically switching phenotypes or noisy transcriptional
circuits provide efficient mechanisms for adapting to
fluctuating or unpredictable environments [33–35]. It
has also been shown that FFL circuits that incorporate
miRNA can dampen noise [36], while certain FFL architectures have specific noise profiles [37] and may be
selected for these profiles, thereby facilitating the
sampling of multiple phenotypes [38].
While it is uncertain whether such phenomena play a role
in T cell biology, it is clear that the transcriptional
Figure 2
Noise
Naive
0.15
σ2
μ
0.10
% Genes
0.10
NE
(6783)
“On”
“Off”
0.05
0.05
% Genes
(a)
0.15
Cell Fate
LE
(2781)
-10
-5
0
5
10
Log(expression value)
HE
(9413)
0.00
0.00
Transcriptome
Th2
σ2
μ
-10
-5
Intermediate
(3030)
0
5
Expression
Classes:
NE - none
10
Log(expression value)
Gata3
LE - low
Intermediate
Gata3
II5
Network
HE - high
II4
Dec2
JunB
Transcriptional
Regulation
JunB
(b)
FFL
σ2
μ
Switch
Epigenetics
(c)
H3K4me3
H3K27me3
Bivalent
Closed
“Off”
Open
“On”
Switches in cellular fate as reflected in the transcriptome, regulatory network, and epigenetic state of the cell, each of which is influenced by noise
(indicated by the Fano factor s2/m). (a) A density plot illustrating highly and lowly expressed transcripts (HE and LE, respectively), transcripts with no
detectable expression (NE) and transcripts with intermediate levels (numbers of genes are in brackets). (b) Transcriptional feedforward circuits involved
in the regulation of key cytokines that facilitate the ‘switch’ to Th2 from naı̈ve cell [24]. Three feedforward loops are illustrated: Dec2-Gata3-Il4, Dec2Gata3-Il5, and Dec2-Jun2-Il4. (c) Histone modifications also play a role in mediating phenotypic switches, with activating histone medications (e.g.
H3K4me3) being associated with an open chromatin state and HE transcripts, while repressive modifications (e.g. H3K27me3) are associated with LE
transcripts. Bivalent chromatin is associated with transcripts that lie between HE and LE in expression level (intermediate).
regulatory networks of CD4+ differentiation are subject
to intrinsic noise and the fundamental limit of noise
suppression mentioned above.
The role of epigenetic switches in CD4+ cells
One mechanism that could have evolved in order to permit
a cost-efficient control of such transcriptional noise could
be the system of epigenetic modifications. Genes that
become differentially expressed during Th differentiation
are accompanied by changes in the chromatin landscape
[19]. For instance, the chromatin at subtype-specific loci
becomes relaxed, as illustrated by DNaseI hypersensitive
regions. Other subtype-specific genes are marked with
repressive histone modifications in cell types not expressing them [39]. Mariani et al. showed that in Th2 cells,
stochastic Il4 expression patterns can be explained computationally using a two-state model, where promoter
activation/inactivation states are explained in terms of
chromatin dynamics (opening and closing due to activating
or suppressing histone modifications). Here, different
populations of cells are created stochastically over generations due to chromatin dynamics [6,40]. This work
provides support for a link between noise in gene expression levels and histone modification levels.
Similarly, our recent work relates gene expression with
histone modification levels, confirming such a two-state
model. In this study, we identified groups of lowly
expressed (LE) and highly expressed (HE) genes in
Th2 cells based on the global distribution of gene expression levels measured by RNA-sequencing (RNA-seq)
[29], which is illustrated in Figure 2a. Here, we annotated
another subset of genes (derived from the LE group) called
not expressed (NE). NE genes are defined as genes with no
reads mapping to that particular transcript, which is dependent on sequencing depth. This concept is similar to
the three expression classes of mRNA previously identified
by Hastie and Bishop [41]. While the HE genes featured
the activating histone mark H3K9/14 acetylation at their
promoters, the LE genes did not. The existence of the
lowly expressed, tissue-specific LE group of genes
suggests that the cell robustly tolerates a certain amount
of background expression, representing a type of ‘transcriptional noise.’ This is often referred to as leaky expression and might be due to a tradeoff between the cost of
(nearly) completely repressed transcription and the
adverse consequences of stochastic expression [42,43].
The leaky expression reflects the inactive promoter state
in the two-state model, while HE transcripts are more
representative of the active promoter state.
In Figure 3, we analyzed the chromosomal organization of
NE, LE, and HE genes. As illustrated in Figure 3a, the
physical distances between adjacent HE and LE genes
were found to be less than between HE and NE genes.
This suggests that expression in LE genes may be due to
their proximity to HE genes. Similarly, as depicted in
Figure 3b, both HE and NE genes tend to form homogenous multigene clusters more often than expected by
chance. In contrast, LE genes usually occur alone. These
findings support a genomic organization illustrated in
Figure 3c, with LE genes close to clusters of HE genes,
and clusters of NE genes further away from HE gene
clusters. In this model, expression from neighboring HE
genes leads to leaky expression of neighboring LE genes
via some form of chromatin state spreading.
An interesting aspect of histone modifications is the
existence of ‘bivalent’ genes, which feature both activating and repressive marks. In mammalian cells, there is
increasing evidence from sequential ChIP and other
experiments that these marks are present simultaneously
at the same allele [44–48]. In Drosophila and Xenopus, it is
unclear whether this is the case [49–51]. In mouse
T-helper cells, Wei et al. observed such bivalent marks
at master regulator loci [22]. The authors argued that such
bivalent marks could poise them for expression. This
could explain the plasticity of these cell types as well
as why perfectly ‘pure’ cultures of single CD4+ subtypes
are difficult to obtain. Using CD4+ microarray expression
and H3K4me3/H3K27me3 histone modification data
(analyzed with Epichip [52]) from Wei et al., we found
that genes with bivalent marks have a higher probability
of differential expression upon maturation than genes
with the H3K27me3 mark or no modification (see
Figure 3d). They are also enriched in TFs relative to
the genomic background. Whether these bivalent genes
also have a specific noise profile is yet to be elucidated.
The functional relevance of noise in
transcriptional regulatory networks
The question remains concerning how much of the
observed stochasticity in CD4+ gene expression is tolerated and how much is beneficial. With regard to the
beneficial aspects of stochastic expression, Guo et al.
argued that probabilistic expression of the Th2 cytokine
Il4 by a fraction of cells is a more flexible mechanism of
immune regulation than fine-tuning of the exact Il4
amounts secreted by a homogenous cell population
[40,53].
Another recently published study on CD8+ cells, closely
related to CD4+ cells, demonstrates how a certain amount
of ‘controlled’ randomness in protein expression generates useful variation in the antigen responsiveness [54] of
the cell. This system is based on a combination of positive
and negative feedback loops and demonstrates that the
architecture of the regulatory circuits can have a profound
impact on the existence of stable states and/or extent of
noise. In line with this, a recent analytical study showed
that feedback systems provided by cytokine secretion and
stimulation, including downstream transcriptional interactions, are sufficient for yielding mathematically stable
states that resemble cell types among CD4+ cells. The
0.2
Figure 3
(a)
(b)
NE-HE median
% HE
70
% LE
60
% Random HE
0.15
% NE
% of genes
LE-HE median
NE-HE mean
0.1
Density
80
LE-HE mean
0.05
LE-HE
% Random LE
50
% Random NE
40
30
20
NE-HE
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0
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5
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2
1
3
4
5
NE-HE
(c)
6
7
8
9 10 11 12 13 14 15
Group Size
Log2(Physical Distance)
NE-HE
3’
5’
LE-HE
LE-HE
(d)
(# Transcription
Factors)
Bivalent
(234)
(162)
H3K27me3
(302)
(145)
H3K4me3
(517)
(410)
HE
LE
NE
% of all mouse genes with mark in
naïve cells
None
% of mouse genes with mark that are
differentially expressed from naïve to
another cell type (excluding nTreg)
20
40
60
(114)
(75)
80
% of genes
The role of highly and lowly expressed transcripts. (a) The physical distances (defined as the shortest genomic distance between genes in nucleotides)
between adjacent pairs of highly expressed (HE) and not expressed (NE)/lowly expressed (LE) genes in mouse Th2 cells. RNA-seq derived gene
expression levels from Hebenstreit et al. [29] were classified as LE or HE; Tophat [63] was also used for junction mapping. In general, LE genes are
found closer to HE genes than NE genes. The difference between the LE–HE versus NE–HE distributions is statistically significant (Wilcoxon rank sum
test, P < 104). This observation may support the concept of leaky expression, or the ripple effect [42,43]. (b) The percentage of all NE, LE, and HE
genes (with intermediate genes between LE and HE being classified as HE) in the genome that occur in a particular cluster size, compared to the
cluster sizes obtained by averaging values across 10 000 randomized sequences of NE, LE, and HE genes. HE and NE genes are significantly more
likely to form larger clusters than expected by chance (P < 104 estimated from simulation). (c) A model describing the organization of NE, LE, and HE
genes according to their distances and clustering. (d) Data from Wei et al. [22] were normalized using MAS5 [64], and differential expression between
naı̈ve and Th1, Th2, Th17, and iTreg was calculated using Limma [65] (an adjusted P-value <0.05 was used to determine significance). More than half
of the genes with bivalent histone marks are differentially expressed upon CD4+ cell maturation. As well, genes with bivalent modifications were
enriched in transcription factors (annotated by DBD [66]) relative to the whole mouse genome (Fisher’s exact test, P < 104).
model also allows for plasticity and gives rise to intermediate cell types [55].
Experimental studies have indeed identified various
circuits involved in CD4+ cell differentiation, which
probably have important roles in regulating transcriptional and thus phenotypic noise. Djuretic et al. noted
that in Th1 cells, the TF Tbx21 induces another TF
Runx3, and together these molecules repress Il4 by
binding to its silencer [56] and activate Ifng by binding
to its promoter. Hence, Tbx21 and Runx3 form FFLs
with Il4 and Ifng. In Treg cells, it has been shown by
Bruno et al. that the TF Runx can promote the expression
of the master regulator Foxp3 [57], and both of these TFs
regulate target cytokines such as Il2 thereby forming a
coherent FFL.
FFL circuits also play an important role in phenotype
determination in biological systems other than CD4+
cells. Foxp3 is known to suppress the oncogene Satb1.
Additionally, it was recently found that Foxp3 also forms
coherent FFLs involving miRNAs that bind to the 30 UTR of Satb1 (miR-7 and miR-155) [58] in breast tissue.
Satb1 also plays an important role in CD4+ cell biology,
particularly cytokine regulation. As noted previously, the
Th2-specific cytokines Il4 and Il5 are regulated by FFL
circuits (see Figure 2) [24]. At the epigenetic level, Satb1
remodels chromatin to create a 200 kb transcriptionally
active region associated with Stat6 and Gata3. These TFs
physically bridge this region with the Th2 ‘cytokine
locus,’ which contains the genes Il4, Il5, and Il13. Cai
et al. showed that this Satb1 mediated remodeling, in
addition to the presence of Stat6 and Gata3, is essential to
cytokine induction [59].
Finally, the regulation of cytokine receptors plays an
important role in the circuits that stabilize Th lineages.
This is supported by the aforementioned modeling
approach [55]. A well-known biological example includes
the IL-4 receptor a-chain (Il4ra), which is upregulated by
IL-4 via Stat6 [60,61] and thus constitutes a Th2-reinforcing positive feedback loop.
Acknowledgements
We thank Guilhem Chalancon, Jesper Nissen and Valentina Proserpio for
helpful suggestions concerning this manuscript. The authors acknowledge
the Lister Institute (UK), the Medical Research Council (UK) grant
reference nos. U105161047 and U105185859, and the Clinician Investigator
Program at the University of British Columbia (Canada) for their support.
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