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K.F. was supported by grants from the Brain Tumor Center
and the Center for Stem Cell Biology at Memorial Sloan
Kettering Cancer Center; C.D.A. is supported by The Rockefeller
University. Funding in part from The Geoffrey Beene Cancer
Center, The Starr Cancer Consortium and the New York State
Stem Cell Board (NYSTEM). Microarray data generated in
this manuscript are deposited in GEO (GSE55541). We thank
J. Huse, Sloan Kettering, for the pathology assessment of the
mouse tumors, and M. Monje, Stanford University, for the
human DIPG line, which is available from her under a material
transfer agreement. V. T. and K.F. are coinventors on a patent
application filed by Memorial Sloan Kettering Cancer Center
related to the use of the MI-2 drug in tumors and the
ES-based modeling system.
Materials and Methods
Figs. S1 to S16
Tables S1 to S6
24 March 2014; accepted 5 November 2014
Published online 20 November 2014;
Promoter architecture dictates
cell-to-cell variability in
Daniel L. Jones,1 Robert C. Brewster,1,2 Rob Phillips1,2†
Variability in gene expression among genetically identical cells has emerged as a central
preoccupation in the study of gene regulation; however, a divide exists between the
predictions of molecular models of prokaryotic transcriptional regulation and genome-wide
experimental studies suggesting that this variability is indifferent to the underlying regulatory
architecture. We constructed a set of promoters in Escherichia coli in which promoter
strength, transcription factor binding strength, and transcription factor copy numbers are
systematically varied, and used messenger RNA (mRNA) fluorescence in situ hybridization to
observe how these changes affected variability in gene expression. Our parameter-free models
predicted the observed variability; hence, the molecular details of transcription dictate
variability in mRNA expression, and transcriptional noise is specifically tunable and thus
represents an evolutionarily accessible phenotypic parameter.
The single-molecule events underlying gene xpression, such as transcription factor binding and unbinding or RNA polymerase (RNAP) open complex formation, are inher- ently stochastic—a stochasticity inherited by
gene expression itself. Over the past decade,
theorists have sought to elucidate how changes
in molecular kinetic parameters such as transcription factor binding and unbinding rates
affect variability in expression (1, 2), whereas experimentalists have measured variability in gene
expression at both the mRNA and protein level in
prokaryotes and eukaryotes (3–6). Possible phenotypic consequences (4, 7–9) include the intriguing hypothesis that transcriptional noise may
increase the fitness of microbial populations by
providing phenotypic variability in a population
of genetically identical cells (10, 11).
Models of transcription hinge on the molecular details of the promoter architecture (where
“promoter architecture” refers collectively to the
locations and strengths of transcription factor
and RNAP binding sites governing a particular
gene) and make quantitative predictions for the
dependence of the variability on these details.
For example, two extremely common promoter
architectures (12) are shown schematically in
Fig. 1A. Here, each rate parameter (r, kR off, kR on,
and g) has a physical interpretation (Fig. 1B) as
an element that can be tuned independently by
genetic manipulation. The effect of promoter
architecture on mean levels of gene expression
is well established in prokaryotes, where thermodynamic models successfully predict gene expression as a function of promoter architecture
(13–15). However, the associated predictions for
how transcriptional noise depends on these parameters remain untested in any systematic way. In
direct contrast to such models, some high-throughput
experiments have culminated in the assertion
that the cell-to-cell variability in gene expression
is “universal,” dictated solely by the mean level of
expression and insensitive to the details of the
promoter driving the expression (3, 5, 6).
To confront this divide, we constructed a library
of synthetic promoters driving a LacZ reporter
in E. coli and measured the resulting mRNA copy
number distributions using single-molecule mRNA
fluorescence in situ hybridization (FISH) (16). Our
approach ensures that differences in promoter se-
quence between constructs have clear interpretations
in terms of the molecular parameters underlying
transcription (e.g., transcription factor unbinding
rate, basal transcription rate). This allows us to
directly compare predictions of models incorpo-
rating those parameters with experimentally ob-
served mRNA distributions, and hence to directly
link the molecular events underlying transcrip-
tion with observed variability in gene expression.
For the case of constitutive expression, shown
schematically in Fig. 1A, mRNA transcripts are
produced and degraded stochastically at rates r
and g, respectively, with constant probability per
unit time. It can be shown (17) that the resulting
steady-state mRNA copy number distribution is
given by a Poisson distribution with mean r/g.
In the following experimental results, we use
the Fano factor, defined as the variance divided
by the mean, to characterize variability in gene
expression. This metric reports the fold change
in the squared coefficient of variation (CV2 =
variance/mean2) with respect to a Poisson pro-
cess, for which CV2 Poisson = 1/mean; hence, CV2/
CV2 Poisson = variance/mean. Therefore, the pre-
dicted Fano factor for constitutive expression
equals 1 identically. However, this analysis is
incomplete: The schematics of Fig. 1A represent
the dynamics of the stochastic processes (tran-
scription factor binding and unbinding, mRNA
degradation, transcription initiation) that con-
tribute to so-called “intrinsic” variability in gene
expression, but do not account for the fact that
rate parameters such as the repressor binding rate
kR on and transcription rate r are themselves subject
to fluctuations due to cell-to-cell variability in
repressor and RNAP copy numbers, respectively.
Such effects, collectively termed “extrinsic variabil-
ity,” tend to increase the measured variability (18).
One important contribution to extrinsic noise
comes from variability in gene copy number due
to chromosome replication (Fig. 2A, bottom panel).
It can be shown (16) that the effect of gene copy
number variation on the variability in expression
is independent and additive to the variability predicted from transcriptional noise, such that
Fano ¼〈m2〉1 − 〈m〉2 1 〈m〉1
þ fð1 − fÞ
1 þ f 〈m〉1
Gene copy number
where 〈m〉1 is the mean mRNA copy number
from a single gene copy, and f is the fraction of
1Department of Applied Physics, California Institute of
Technology, Pasadena, CA 91125, USA. 2Division of Biology,
California Institute of Technology, Pasadena, CA 91125, USA.
*These authors contributed equally to this work. †Corresponding
author. E-mail: firstname.lastname@example.org