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PERSPECTIVE
From the Departments of Biochemistry and
Microbiology/Parasitology and the Centre for Molecular and Cellular
Biology, University of Queensland, Queensland, Australia.
The phenotype of individual hematopoietic cells, like all other
differentiated mammalian cells, is determined by selective transcription of a subset of the genes encoded within the genome. This
overview summarizes the recent evidence that transcriptional regulation
at the level of individual cells is best described in terms of the
regulation of the probability of transcription rather than the rate. In
this model, heterogeneous gene expression among populations of cells
arises by chance, and the degree of heterogeneity is a function of the
stability of the mRNA and protein products of individual genes. The
probabilistic nature of transcriptional regulation provides one
explanation for stochastic phenomena, such as stem cell lineage
commitment, and monoallelic expression of inducible genes, such
as lymphokines and cytokines.
(Blood. 2000;96:2323-2328) Even the simplest organisms are able to modify the
expression of certain genes in response to environmental signals such
as nutrient levels. In complex multicellular organisms, selective gene
expression is absolutely required for cellular differentiation and
organogenesis and for homeostasis. Much of this regulation occurs at
the level of transcription initiation; transcriptional regulatory
proteins bind to the promoters of appropriate target genes and increase
or decrease the amount of mRNA that is produced. Transcriptional
regulation could be viewed as an analog process, akin to depressing the
accelerator on a car, or a digital process, like switching on a light.
In either a digital or an analog model, an increase in transcription in
a single cell when a stimulus is added might occur as a direct and
predictable response to a stimulus or in a probabilistic manner (the
stimulus increasing the probability of response within a given time).
The language of gene regulation generally assumes an analog model and
direct causation; a transcription-activating factor is said to increase the rate of transcription. In this essay, I will present some of the
evidence that transcription is actually a digital process, and I will
argue that it is more meaningful to talk about the probability and
frequency of transcription rather than the rate. In the second half of
the essay, I will examine how such a view of transcription can change
the way we interpret studies of inducible gene expression, cellular
heterogeneity, and lineage determination using hematopoiesis and
activation of T cells as examples.
Transcription happens!
Biochemical studies of transcription initiation support a digital
mechanism. The assembly of the transcription apparatus on a DNA
template is unstable on a short time scale. Kadonaga2 showed that the complex transcription apparatus must be reassembled between each "round" of transcription. Reassembly of a
preinitiation complex occurred in vitro with a half-time of 3 minutes;
thereafter, initiation occurred in seconds. It is important to
recognize that the transcription machinery in such in vitro
transcription systems is in large excess. Despite this excess, Bral et
al3 found that only a small proportion of the templates in
the assay were successful in forming an active preinitiation complex.
They propose that templates partition in a binary manner into active
and inactive complexes. In a single cell, with only 2 DNA templates
available for each specific gene, the formation of 2 inactive complexes would mean that the transcription of that gene is completely switched off. The question is, how long does it take, within an intact cell, to
detach and disassemble a failed complex and try again?
The answer may be, quite a long time. The availability of antibodies
against active phosphorylated RNA pol II, as well as other
transcription factors or approaches to identifying nascent transcripts,
has made it possible to localize the sites of transcription in the
nucleus.4,5 Transcription seems to occur in specific physical structures within the nucleus, referred to as transcription factories.4-6 The number of sites of active pol
II-mediated transcription in the nucleus was estimated to be
approximately 2500.4,7 If this is the case, only a subset
of protein-encoding genes is likely to be actively transcribed at any
time. Aside from restrictions on the absolute availability in numerical
terms, there is also evidence for functional compartmentation of
transcription, processing and trafficking in the mammalian nucleus
(reviewed in 8). Both RNA pol II and the splicing and
processing factors required for co-transcriptional mRNA processing
appear to be concentrated in discrete nuclear domains, sometimes
referred to as speckles, of which there may be as few as 20 to 40 per
nucleus. Translocation away from those domains is correlated with the
activation of transcription.9-11 Exactly how a DNA
template identifies itself as a target for association with these
components is beyond current understanding, but one can imagine the
additional constraints on the probability of this event if RNA pol II
templates are physically separated from the transcription apparatus.
A digital mechanism of transcriptional activation has been confirmed
using single-cell imaging technology to study the process of
transcription of As an alternative to direct visualization of events on individual
templates within the nucleus, several groups have performed single-cell
analyses of reporter gene expression. This approach introduces an
additional level of complexity, because the relationship between the
number of cells with detectable reporter gene product is clearly a
function of the stability of the mRNA and protein and of
transcriptional activity (see below). We first performed single-cell
analyses using the HIV-1-LTR driving lacZ, stably transfected
into RAW264 macrophages.17 In the absence of the Tat
trans-activator, the frequency of strongly lacZ-positive
cells was as low as 10 Evidence that gene expression is best described in terms of probability
has also been provided from real-time imaging experiments. White et
al21 described a system for determining the activity of
luciferase reporter genes in transfected HeLa cells. They observed directly that the HIV-1-LTR and cytomegalovirus promoters
shuttle on and off in individual cells. Subsequently, Takasuka et
al22 used the same approach to study the regulation of the
prolactin promoter in individual cells from a cloned pituitary cell
line. Again, a profound variation without obvious pattern was observed between individual cells responding to stimuli that activate promoter activity.
Hence, a large body of evidence indicates that transcription is a
digital process by which individual DNA templates exist in an off state
and the likelihood of switching to the on state is regulated. If we
accept that premise, we must use a new language to describe
transcription. The production of mRNA occurs in pulses. The
mean frequency of pulses is the major determinant of mRNA production and is determined by the probability of formation
of a preinitiation complex. The average number of mRNA molecules produced in each pulse (which could be referred to mean
amplitude of a pulse) is determined by the stability of the
preinitiation complex, the probability of formation of a
dead-end preinitiation complex in each successive round of
reinitiation, or both. The mean amplitude, judging from the
studies of Regulation of transcription: probabilities multiply!
The cis-acting elements recognized by transcription factors
are commonly grouped in the vicinity of proximal promoters or enhancers. As predicted from the in vitro actions of transcription factors, compound elements such as enhancers act to increase the probability of transcription in intact cells.42,43 Within
such enhancers, individual DNA-binding proteins may bind to each other and to DNA or may display interdependent binding activities. One relevant example is the complex IL-2 enhancer, where the binding or
mutation of any cis-acting element abolishes binding of
transcription factors to all the other sites.44 The
binding of transcription factors to intact chromatin, as opposed to
naked DNA, may, in fact, be inherently cooperative.45
There is also growing evidence for crucial roles for co-activator (and
co-repressor) proteins in linking the binding of transcription
activators to their individual response elements to the formation of an
active preinitiation complex39 or reinitiation on the same
template.13 Each of these kinds of interaction can lead to
genuine cooperativity or synergism in which transcription does not
occur at all unless the entire machinery is available. However, the
simple observation that 2 stimuli act multiplicatively is commonly
taken as evidence of synergism, with an inferred mechanistic basis (eg,
some form of protein-protein interaction).46 An important
corollary of the probabilistic view of transcription is that
combinations of independent elements (or signals) generate sigmoidal
dose response curves inherently because probabilities multiply. A model
of a hypothetical "probability-driven" promoter is presented in
Figure 1. Based on our experience with
the HIV-1-LTR, a minimal promoter might have an intrinsic probability
of transcription of one event in 104 templates per hour. In
the model, each enhancer element occupied by a transcription factor
produces a 5-fold increase in transcription probability (again, a
realistic number based on our own experiences of transcriptional
activators). Hence, if any 6 of those elements is occupied (say in
response to an extracellular signal), the probability increases
56-fold, so that most cells in the population will produce
at least one pulse of transcripts within a 1-hour period. According to the probabilistic model, simple multiplicative actions of combinations of signals at any level in a transcriptional regulatory pathway can
actually be taken as evidence against any direct interaction between
them.
Importance of mRNA stability Each time a cell makes a pulse of a specific mRNA, it accumulates in the cytoplasm and is translated. Eventually the mRNA and protein decay. So, the time between pulses of transcription is characterized by a decay profile. If the mRNA and protein products are stable, the range of expression of individual genes in a population of cells will be small even if the gene is transcribed infrequently. For example, despite the absence of detectable active transcription, the level of -actin mRNA in serum-starved fibroblasts was estimated at
500 ± 200 molecules per cell, rising to approximately 1500 after a
pulse with serum.12 If the half-life of -actin mRNA is
of the order of 10 hours, only occasional pulses of hundreds of mRNA
molecules would be required to maintain that differential in the
serum-stimulated steady state. The importance of mRNA stability is
illustrated in another study by Kringstein et al,47 who
sought to examine the relation between transcription factor
concentration and reporter gene production. The model system involved
the use of a tetracycline-inducible system and a green fluorescent
protein (GFP) reporter. Addition of activator was found to cause a
step-wise dose-dependent increase in expression per cell, when assayed
by flow cytometry. Their finding does not argue against an
all-or-nothing process of transcriptional activation on single
templates. It means simply that the GFP reporter is stable and
accumulates in the cell in proportion to the number of rounds of
transcription that occur within the time frame studied.
Conversely, a much broader distribution of levels of expression, approaching a true bimodality, will be observed if the mRNA and protein products of a gene have half-lives significantly shorter than the average time between rounds of transcription. In these circumstances, the level of mRNA and protein in single cells becomes a function of the time that has elapsed since the previous pulse of mRNA. Clearly, the implication is that differential gene expression between "subpopulations" need not reflect some specific specialization; if the gene product has a short half-life, it just happens! Stochastic regulation in hematopoiesis A major issue in stem/progenitor cell fate determination in hematopoiesis concerns whether regulatory factors "instruct" target cells to commit to particular blood cell lineages or "select" cells that have already chosen a particular path that includes expression of the lineage-restricted receptor. There is a considerable body of evidence implying that lineage decisions in hematopoiesis arise probabilistically and that growth factors act to promote the survival and growth of cells in which the "decision" has already been made.48,49 A direct demonstration is found in a transgenic mouse expressing the human granulocyte-macrophage colony-stimulating factor (GM-CSF)-R, wherein human GM-CSF directed differentiation of a wide range of hematopoietic lineages.50 This view point does not argue that colony-stimulating factors, or other regulators in the hematopoietic environment, have no function in lineage determination. The key distinction is between direct and predictable causation and the regulation of probability; a specific colony-stimulating factor may (and probably does) influence the likelihood that an individual target cell will follow a particular differentiation pathway.The digital model of transcriptional regulation implies that individual genes can shuttle on and off in real time. If the gene products of key genes in hematopoietic commitment are unstable and transcribed sufficiently infrequently, then random pulses of mRNA could provide an explanation for heterogeneous expression and stochastic behavior. Stem cell "commitment" might arise through the co-expression, by chance, of combinations of lineage-specific transcription factors and hematopoietic growth factor receptor(s). At any early stage of commitment, any growth factor may suffice because it is the transcription factors that determine the cellular phenotype. Subsequently, the transcription factors will increase the probability of expression of lineage-restricted growth factor receptors (the promoters of which commonly contain binding sites for lineage-restricted transcription factors), further reinforcing the commitment event. Such a mechanism could only operate if most of the genes required for hematopoietic lineages were in open chromatin in stem cells and available for transcription, albeit with relatively low probability. The prediction of such a model is that stem cells and early "committed" progenitor cells would be extremely heterogeneous in gene expression. Iscove's group51 developed single-cell-polymerase chain reaction approaches to identify mRNA expression in single cell-cloned hematopoietic progenitor cells. Their data revealed a fundamental randomness to co-expression of lineage-specific genes in specific lineage-committed colony-forming cells. Similarly, Hu et al52 found evidence of a so-called promiscuous phase of multi-lineage gene expression in which myeloid and erythroid lineage genes, including growth factor receptors, are co-expressed. The most common assay of the phenomenon of hematopoietic commitment, the soft-agar cloning assay, is obviously binary in nature; hematopoietic growth factors are assayed based on the number of colonies they induce, and colonies are arbitrarily defined in size. A purely probabilistic model based on the chance expression of receptors predicts that the addition of more than one factor will have an additive effect on colony number. Remarkable cocktails of factors (IL-1, IL-3, IL-6, SCF, GM-CSF, CSF-1, and so on) are indeed added to colony assays to maximize the number of colonies formed.49 Recently, McKinstry et al53 examined directly the expression on sorted hematopoietic stem/progenitor cell pools of receptors for a wide range of such factors. As predicted, they observe considerable heterogeneity in the percentage of cells labeled and the number of receptors per cell. They propose to determine whether such subpopulations have distinctive functional properties. If the subpopulations are "snapshots" of co-expression of certain genes arising by chance, such an approach may not be productive. Inducible gene expression in lymphocytes Several of the reporter genes discussed in the first section represent models of highly inducible genes. Differentiated cell types must maintain such genes in open chromatin but only express the gene product in response to an appropriate external stimulus. In response to that stimulus, they must rapidly increase the probability of transcription of the gene. One well-studied example of inducible gene regulation occurs in stimulated T lymphocytes. Activated T cells produce numerous different lymphokines. The prevailing paradigm is that subsets of activated T cells, termed Th1 and Th2 cells, produce different sets of lymphokines that polarize the immune response toward cell-mediated or humoral effector mechanisms, respectively. There has been a concerted effort to identify markers that define the Th1 and Th2 subsets of cells.54 Of course, the tendency for 2 genes to be co-expressed, at both a single cell and a population level, can be greater than the product of 2 transcription probabilities. IL-4 and other Th2 lymphokines will be more likely to be produced together than predicted by chance if the genes that encode them share cis-acting elements. The drive toward co-expression can be amplified if the products of one gene, such as IL-4, activate signaling pathways that increase the probability of expression of other Th2-associated genes. Nevertheless, single-cell analysis has provided no evidence to support an absolute Th1/Th2 dichotomy.55-58 In fact, in studies of T cells activated in a T-cell receptor transgenic mouse, co-expression of any 2 lymphokine genes by individual T cells appeared to be the exception.59-61 Bucy et al62 noted that during the development of so-called Th0 cell clones from such mice, each cytokine mRNA exhibited its own characteristic expression profile, and the major effect of increasing antigen dose was to "recruit" additional mRNA-expressing cells. Even in model systems in which stimuli were chosen to polarize the T-cell response selectively in the Th1 or Th2 direction, and in which selective cytokine production was demonstrable at the population level, single-cell mRNA analysis revealed that each gene is expressed with its own independent probability.56 If this heterogeneity simply reflects the probability of assembly of inducible genes in active chromatin or assembly of the transcription complex once this has occurred, there is an obvious corollary. Unless the probability is very high, only one allele of any gene is likely to be transcribed in any one cell. Indeed, that prediction has recently been confirmed with the finding that inducible IL-2 and IL-4 production can be monoallelic in individual T cells.63,64 Similarly, in transgenic mice in which 1 allele of the IL-2 gene was replaced with a GFP reporter gene by homologous recombination, only a subset of individual activated T cells expresses the GFP reporter.65 Bix and Locksley66 confirmed the prediction that the pattern is random by identifying both bi-allelic and monoallelic expression of IL-4 among CD4-positive T-cell clones.I predict that the availability of probes that distinguish the mRNA products of each allele in heterozygous individuals will show monoallelic expression to be a general phenomenon in inducible genes that has nothing to do with specific regulation. An interesting consequence of monoallelic expression arises when one of the alleles is dysfunctional. The phenomenon of haploinsufficiency in genetic disease is generally considered to reflect a situation in which 50% of the normal level of gene product is insufficient to carry out normal function. Others have recognized67,68 that at a single-cell level, haploinsufficiency can mean complete insufficiency if the active allele is sequestered into inactive chromatin, or if the gene product is relatively unstable and the gaps between rounds of transcription are extended.
Because transcriptional regulation is a process that underlies every aspect of eukaryotic biology and development, an acceptance of probabilistic basis for the process begs questions as to how order of any kind can be achieved. As noted by McAdams and Arkin,69 stochastic patterns of gene expression can produce predictable outcomes with respect to particular alternative pathways. The uncertainty of transcription in eukaryotes is partly overcome by having 2 alleles at each locus and by the redundancy that seems particularly prevalent among transcription regulatory proteins in higher organisms. It is also overcome by having self-amplifying autocrine loops that are remarkably prevalent in systems such as the activation of macrophage cytokine production.70 The certainty of activation of the gene in Figure 1 becomes much greater if each of the transcription factors also increases the probability of binding of the others (by protein-protein mechanisms or by increasing the probability of expression of other transcription factor genes) and if the gene product acts through autocrine/paracrine mechanisms to increase the frequency of activation of other alleles of the same gene. Finally, in real promoters, the number of transcription control elements can be remarkable. For example, in the inducible urokinase plasminogen activator gene in macrophages, we have described highly conserved transcription control elements extending up to 8 kb 5' of the transcription start site.71,72 If it simply had to be transcribed, the model gene in Figure 1 could have 20 control elements, so that occupation of any 10 of them would make transcription a certainty in biologic time. Uncertainty could have its advantages. For example, if the mRNA and
protein encoding a specific cell surface receptor is unstable, the
level of receptor on each cell in the population will be heterogeneous. If a particular biologic response occurs in response to threshold level
of occupied receptor, the number of responding cells will increase with
agonist concentration. At another level, if the receptor interaction
gives a quantitative signal, the fact that each gene will have its own
unique transcription probability dictated by the cis-acting
elements it contains will dictate that each gene have its own unique
dose-response curve to agonist. There are numerous examples in
developmental biology that show concentration-dependent cell activation
is required to allow a cell to determine its position in a gradient of
a diffusible regulator. One example from cytokine regulation is the
induction of TNF-
I have argued that transcription initiation in higher eukaryotes occurs with a relatively low frequency in biologic time and that the process is regulated in a probabilistic manner. In this model, the stability of the mRNA and protein products of a gene becomes crucially important. Some predictions of this model have been confirmed in hematopoietic cells; wherever single-cell (or single-allele) analysis is performed, the expression of each gene is seen to be regulated stochastically. The model changes the way we interpret the heterogeneity of cells in hematopoietic/immune systems. It may provide an explanation for the remarkable plasticity of stem cells,75 which is one of the most exciting areas of current biology.
I thank Professor Alan Wolffe (National Institutes of Health, Bethesda, MD), Professor Anne Kelso (Queensland Institute for Medical Research, Brisbane, Australia), and Dr Jerry Molitor (University of Washington, Seattle, WA) for critical reading and helpful comments. I also thank the reviewers of Blood, who decimated the first version of this manuscript, hopefully to good effect.
Submitted August 9, 1999; accepted April 18, 2000.
Supported by the National Health and Medical Research Council and the Queensland Cancer Fund. The Centre for Molecular and Cellular Biology is a Special Research Centre of the Australian Research Council.
The publication costs of this article were defrayed in part by page charge payment. Therefore, and solely to indicate this fact, this article is hereby marked "advertisement" in accordance with 18 U.S.C. section 1734.
Reprints: David A. Hume, Department of Biochemistry, University of Queensland, Q4072 Queensland, Australia; e-mail: d.hume{at}cmcb.uq.edu.au.
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© 2000 by The American Society of Hematology.
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E. D. Hawkins, M. L. Turner, M. R. Dowling, C. van Gend, and P. D. Hodgkin A model of immune regulation as a consequence of randomized lymphocyte division and death times PNAS, March 20, 2007; 104(12): 5032 - 5037. [Abstract] [Full Text] [PDF]
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V. A. Kuznetsov, G. D. Knott, and R. F. Bonner General Statistics of Stochastic Process of Gene Expression in Eukaryotic Cells Genetics, July 1, 2002; 161(3): 1321 - 1332. [Abstract] [Full Text] [PDF]
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R. D. Smith, J. D. Malley, and A. N. Schechter Quantitative analysis of globin gene induction in single human erythroleukemic cells Nucleic Acids Res., December 15, 2000; 28(24): 4998 - 5004. [Abstract] [Full Text] [PDF]
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Top
Abstract
Introduction
(which is regulated postranscriptionally in
the macrophage cell line used), were induced in all the cells. In
subpopulations of the macrophage cell line used in these studies, the
frequency of expressing cells varied. Hence, PAI-2 mRNA production
occurs in a stochastic manner, with a frequency (probability) that is
determined by the strength of the LPS signal and the state of the cell.
We call this a digital model, because it implies that the transcription
apparatus exists in "on" and "off" states and that regulation
involves switching between those states.
4 in cloned stable transfectants.
Even assuming a short half-life for lacZ protein and mRNA, this implies
that transcription occurs infrequently indeed. In the presence of Tat,
the frequency increased to approximately 10
B, NFAT-1), gave a bimodal distribution of lacZ activity.
Accumulation of transcription factors in the nuclei of activated cells
was correlated with transition between the lacZ-expressing and
nonexpressing "states." Ko et al