ArticlePDF Available

Transcriptional burst frequency and burst size are equally modulated across the human genome


Abstract and Figures

Gene expression occurs either as an episodic process, characterized by pulsatile bursts, or as a constitutive process, characterized by a Poisson-like accumulation of gene products. It is not clear which mode of gene expression (constitutive versus bursty) predominates across a genome or how transcriptional dynamics are influenced by genomic position and promoter sequence. Here, we use time-lapse fluorescence microscopy to analyze 8,000 individual human genomic loci and find that at virtually all loci, episodic bursting-as opposed to constitutive expression-is the predominant mode of expression. Quantitative analysis of the expression dynamics at these 8,000 loci indicates that both the frequency and size of the transcriptional bursts varies equally across the human genome, independent of promoter sequence. Strikingly, weaker expression loci modulate burst frequency to increase activity, whereas stronger expression loci modulate burst size to increase activity. Transcriptional activators such as trichostatin A (TSA) and tumor necrosis factor α (TNF) only modulate burst size and frequency along a constrained trend line governed by the promoter. In summary, transcriptional bursting dominates across the human genome, both burst frequency and burst size vary by chromosomal location, and transcriptional activators alter burst frequency and burst size, depending on the expression level of the locus.
Content may be subject to copyright.
Transcriptional burst frequency and burst size are
equally modulated across the human genome
Roy D. Dara,b,c,1, Brandon S. Razookya,d,e,1, Abhyudai Singhd,2, Thomas V. Trimelonif, James M. McCollumf,
Chris D. Coxg,h, Michael L. Simpsonb,I,3, and Leor S. Weinbergera,d,j,3
aGladstone Institutes, San Francisco, CA 94158; bCenter for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831;
cDepartment of Physics and Astronomy, University of Tennessee, Knoxville, TN 37996; dDepartment of Chemistry and Biochemistry, University of California
at San Diego, La Jolla, CA 92093; eBiophysics Graduate Group, University of California, San Francisco, CA 94158; gCenter for Environmental Biotechnology,
University of Tennessee, Knoxville, TN 37996;hDepartment of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN 37996;
IDepartment of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996; jDepartment of Biochemistry and Biophysics, University
of California, San Francisco, CA 94158; and fDepartment of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA
Edited by Jonathan S. Weissman, University of California, San Francisco, CA, and approved September 13, 2012 (received for review August 8, 2012)
Gene expression occurs either as an episodic process, characterized
by pulsatile bursts, or as a constitutive process, characterized by
a Poisson-like accumulation of gene products. It is not clear which
mode of gene expression (constitutive versus bursty) predomi-
nates across a genome or how transcriptional dynamics are influ-
enced by genomic position and promoter sequence. Here, we use
time-lapse fluorescence microscopy to analyze 8,000 individual
human genomic loci and find that at virtually all loci, episodic
burstingas opposed to constitutive expressionis the predomi-
nant mode of expression. Quantitative analysis of the expression
dynamics at these 8,000 loci indicates that both the frequency
and size of the transcriptional bursts varies equally across the
human genome, independent of promoter sequence. Strikingly,
weaker expression loci modulate burst frequency to increase
activity, whereas stronger expression loci modulate burst size to
increase activity. Transcriptional activators such as trichostatin A
(TSA) and tumor necrosis factor α(TNF) only modulate burst size
and frequency along a constrained trend line governed by the
promoter. In summary, transcriptional bursting dominates across
the human genome, both burst frequency and burst size vary by
chromosomal location, and transcriptional activators alter burst
frequency and burst size, depending on the expression level of
the locus.
stochastic noise automated single-cell imaging human
immunodeficiency virus long terminal repeat promoter
There exists conflicting evidence over the predominant mode
of gene expression in both prokaryotes and eukaryotes. The
classical view of gene expression as a constitutive, Poisson-like
accumulation of gene products (Fig. 1A) is supported by a com-
prehensive large-scale study in bacteria, demonstrating that >400
genes appear to follow constitutive (or Poisson-like) gene expres-
sion (1). Constitutive expression has also been reported for
subsets of human genes (2). Conversely, several elegant studies
showed that specific promoters in bacteria and yeast express
gene products in an episodic process (Fig. 1B), characterized by
pulsatile bursts in transcription (39). Given this conflicting evi-
dence, it remains unclear if episodic bursting is the predominant
mode of gene expression across a genome or just a highlighted
exception. If bursting is predominant, it is not clear if or how it
depends on genomic location.
To globally determine if constitutive Poisson-like expression
or episodic bursty expression dominates throughout the human
genome, we capitalize on a recently proposed theoretical frame-
work (10) for extracting the details of gene regulation from the
time-resolved structure of fluctuations (i.e., noise) in gene expres-
sion. This analysis quantifies time-lapse expression trajectories to
obtain three orthogonal measures of expression: the average
expression level, the magnitude of expression fluctuations (as
measured by the coefficient of variation squared, CV2), and the
autocorrelation time of expression fluctuations (as measured
by the noise autocorrelation time at half of its initial value, τ12)
(11, 12) (Fig. 1C). Although this three-dimensional noise space is
impractical to analyze directly, different two-dimensional projec-
tions of noise space allow the quantification of rate parameters
in gene-regulatory models and provide a convenient method to
differentiate between underlying gene-expression mechanisms,
such as constitutive versus bursty transcription (Fig. 1C). For
example, transcriptional bursting increases both noise magnitude
and noise autocorrelation time and shifts points in the CV2
versus τ12plane to the upper right quadrant relative to a con-
stitutive expression model (Fig. 1C,Bottom Left). Conversely,
translational bursting shifts noise magnitude, but not the auto-
correlation time (10, 13).
Importantly, analysis of the τ12axis is critical to fully parame-
terize two-state transcriptional bursting models (Fig. 1C), which
always include at least three unknown parameters: the rate of
transition to a transcriptionally active state (kon), the rate of tran-
sitioning to a transcriptionally inactive state (koff ), and the rate
of transcription once in the active state (km) (13, 14). Analyses
of a single two-dimensional plane (e.g., CV2versus expression
level) cannot fully determine these three rate parameters. Con-
versely, analyses of CV2versus expression level and τ12versus
expression level allow the determination of these three para-
meters, and analysis of CV2-versus-τ12facilitates direct compar-
isons of data containing widely varying expression levels, because
it removes the reciprocal dependence of noise magnitude on
expression level (10).
The ability to accurately quantify these transcriptional rate
parameters is essential for answering basic questions about the
mechanisms that regulate transcription. Previous studies ele-
gantly applied flow cytometry (6, 7) and time-lapse microscopy
(1, 15, 16) to analyze gene-expression noise in large subsets of
genes. However, a tedious experimental bottleneck of subcloning
and expansion of isogenic populations necessarily limits the
throughput of these noise-analysis approaches. Here, we circum-
vent this subcloning requirement to globally apply the analytical
Author contributions: R.D.D., B.S.R., C.D.C., M.L.S., and L.S.W. designed research; R.D.D.,
B.S.R., T.V.T., and J.M.M. performed research; R.D.D., B.S.R., C.D.C., M.L.S., and L.S.W.
contributed new reagents/analytic tools; R.D.D., B.S.R., A.S., T.V.T., J.M.M., M.L.S., and
L.S.W. analyzed data; and R.D.D., B.S.R., C.D.C., M.L.S., and L.S.W. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
1R.D.D. and B.S.R. contributed equally to this work.
2Present address: Department of Electrical and Computer Engineering, University of
Delaware, Newark, DE 19716.
3To whom correspondence may be addressed: E-mail: or
This article contains supporting information online at
1745417459 PNAS October 23, 2012 vol. 109 no. 43
framework of noise space across the human genome and quantify
these transcriptional rate parameters. The analysis addresses two
specific questions regarding transcriptional regulation in human
cells. (i) Does constitutive (Poisson-like) expression or episodic
(bursty) expression dominate throughout the human genome?
(ii) Does genomic location influence either mode of expression?
For example, if bursting is operant, does genomic location influ-
ence burst size or burst frequency, and which is predominantly
influenced? Recent studies (1719) have tackled these questions
for specific genes, but no clear and broad consensus has yet
To globally apply the analytical framework of noise space to
screen for constitutive versus bursty expression across the human
genome, we capitalized on the semirandom pattern of integration
exhibited by the HIV-1 lentivirus, where the majority of integra-
tions (approximately 69%) occur within transcriptionally active
regions (20, 21). Jurkat T lymphocytes were infected with HIV-
based lentiviral vectors encoding a short-lived, two-hour half-life
version of green fluorescent protein (referred to as d2GFP), to
generate a library of cells in which the vector is integrated at a
different genomic position in each individual cell (i.e., a poly-
clonal library) (Fig. 2A). To focus on measuring the intrinsic fluc-
tuation dynamics of the genomic region surrounding the vector-
integration site, we utilized a vector encoding the HIV-1 LTR
promoter, which is relatively weak and heavily influenced by the
expression dynamics of the local chromatin environment (22).
Initially, the analytical framework for distinguishing bursty
expression was applied to five isoclonal populations: Each popu-
lation was grown from a single parent cell, and all daughter
cells, therefore, share the same LTR genomic integration site
(Fig. 2 AD). Cell fluorescence was then imaged for 18 h, and the
resulting fluorescence intensity trajectories were used to con-
struct a three-dimensional noise space with three axes: noise mag-
nitude as measured by the coefficient of variation squared (CV2),
noise autocorrelation represented by the half-autocorrelation
time (τ12), and mean expression level (hGFPi) (Fig. 2 BCand
SI Appendix). Analysis of these trajectories in the noise space
allows comparisons between isoclonal populations and differen-
tiation between isoclones that exhibit constitutive transcription
versus episodic bursts of transcription (Fig. 2D). To generate an
initial baseline origin for noise-space analysis of the single-cell
data, we identified isoclones that were the most Poissonian in
their expression fluctuation profiles from a library of isoclonal
populations (17) (SI Appendix). Two isoclonal populations exhi-
biting the fastest fluctuation autocorrelation decays (i.e., shortest
autocorrelation times) were selected as the most Poissonian and
used to establish an origin of the CV2-versus-τ12noise map
(Fig. 2D, clones 1 and 2). The isoclone heat map represents the
probability of where a randomly chosen single cell would land on
the noise map (Fig. 2D).
Importantly, nontranscriptional phenomena that influence
noise behavior (e.g., protein and mRNA lifetimes, GFP matura-
tion, extrinsic noise) are already embedded in the noise of these
isoclonal populations, specifically the most Poissonian reference
clones. Thus, comparison between these necessarily precludes the
possibility that noise-map shifts are due to these nontranscrip-
tional phenomena. In addition, the high-frequency noise-proces-
sing technique minimizes extrinsic noise effects (SI Appendix)
(12). Because it is likely that these two isoclones are somewhat
bursty in their expression (SI Appendix), comparisons to these iso-
clones represent a highly conservative assay for bursty expression.
Nevertheless, the results show that even in a small panel of five
clones, there are marked changes in both noise magnitude and
autocorrelation time of approximately 1.5-fold in normalized
space (Fig. 2D). Clones 4 and 5 show significant changes in auto-
correlation time with smaller differences in noise magnitude.
These differences in noise magnitude were validated against con-
ventional flow cytometry measurements (SI Appendix, Fig. S1).
The agreement between data from the 1218 h microscopy
experiments and flow cytometry demonstrates that this CV2-ver-
sus-τ12analysis has the fidelity to differentiate transcriptional
dynamics between different isoclones.
We next extended the analysis to image polyclonal populations
consisting of thousands of integration sites (Fig. 3A)to glob-
ally apply the analytical framework of noise space to screen for
constitutive versus bursty expression across the human genome.
We analyzed more than 8,000 distinct genomic loci with three
different promoters integrated throughout the genome by ima-
ging cells for 1218 h (Fig. 3B). The constitutive expression origin
of the CV2-versus-τ12noise map previously determined for
the most-Poissonian isoclones (Fig. 2Dand SI Appendix) was
compared to the 8,000 loci. To control for LTR-specific or vector-
specific artifacts, we also tested self-inactivating lentiviral vectors
Fig. 1. Fluctuations in gene expression to differentiate between alternate
models of transcription across the genome. (Aand B) Schematics of the con-
stitutive, Poisson-expression model and the episodic, bursty gene-expression
model, together with three expression trajectories from hypothetical geno-
mic loci. Sites that exhibit constitutive (i.e., Poisson) expression exhibit small
and relatively fast fluctuations in gene products over time. Alternatively,
loci that exhibit episodic expression bursts generate large, slow fluctuations
in gene expression. (C) The principle of noise space. The three-dimensional
noise space consists of noise magnitude, noise autocorrelation, and mean
expression level. Small, fast fluctuations have a small noise magnitude and
short autocorrelation times and thus cluster (after normalization) at the
origin of the noise magnitude-autocorrelation plane (gray region, Lower
Left). Large, slow (i.e., bursty) fluctuations have expanded noise magnitude
and extended autocorrelation times (red ovals). The three-dimensional
space can be decomposed into two additional two-dimensional projections
of noise magnitude and noise autocorrelation versus mean expression level
(Lower Center and Lower Right). For episodic-bursty expression, a trajectorys
noise-space coordinates are invariably shifted away from the constitutive
model into the burst model space depending on changes to their transcrip-
tional parameters (10).
Dar et al. PNAS October 23, 2012 vol. 109 no. 43 17455
that encode either promoters for human elongation factor 1α
(EF1A) or human ubiquitin C (UBC) that in turn drive d2GFP.
UBC and EF1A are promoters that drive essential cellular
housekeeping genes: UBC promotes the ubiquitinization cascade
by marking proteins for proteosomal degradation, and EF1A
promotes the GTP-dependent binding of an aminoacyl-tRNA to
ribosomes. UBC and EF1A are among the most abundant pro-
teins in eukaryotic cells, and their promoters exhibit robust high-
level expression across integration sites in different cell types
(23, 24). Virtually all examined genomic loci for all promoters
exhibit noise-map shifts to the upper right (Fig. 3B), indicating
significant bursting in gene expression at virtually all genomic loci.
Similar widespread bursting is also observed in the THP-1 human
monocyte cell line and compared to a constitutive simulation (SI
Appendix,Fig.S2). Both synchronized and unsynchronized cells
exhibit similar shifts in noise space (SI Appendix, Fig. S3), indicat-
ing that transcriptional bursts appear to be common throughout
the cell cycle, as reported (25).
The UBC and EF1A promoters showed markedly lower CV2
values than the LTR promoter (Fig. 3Band SI Appendix, Fig. S4),
which is consistent with our previous study (17) showing that the
LTR promoter displays relatively higher levels of noise than other
eukaryotic promoters in yeast (6, 7). This shift in noise magnitude
is consistent with the well-known transcriptional elongation stall
that characterizes LTR expression (26). This stall results in
Fig. 2. Extracting transcriptional parameters from
the noise space. In individualisoclones, burst dynamics
vary with genomic location. (A)Cellsareinfectedwith
a lentiviral vector expressing a 2-h half-life GFP repor-
ter (d2GFP) at a low multiplicity of infection (moi) to
ensure a single semirandom integration in each cell.
Individual single cells are isolated, grown (creating
isoclone populations), and imaged by time-lapse
fluorescence microscopy. (Band C) Single-cells are
tracked for 1218 h, and an individual cells mean ex-
pression level, variance (σ2), and autocorrelation time
(τ12) are extracted from the time trace (e.g., the
green circle represents a single cells noise space coor-
dinate). A constitutive model of gene expression that
displays abundance dependence (bold red arrows
from black model lines) was used to normalize each
cells noise magnitude (CV2) and autocorrelation
(τ12). The normalized noise magnitudes and autocor-
relations are plotted in a Δlog CV2Δlog τ12noise
map (Left). (D) Consistent shifts to the Upper Right
quadrant in ΔlogCV 2Δlog τ12space observed
for three LTR isoclones (clones 3, 4, and 5), are indica-
tive of transcriptional bursting relative to the least
bursty isoclones (clones 1 and 2). Bursting dynamics
varies between different clones as evidenced by
shifts in both noise autocorrelation and magnitude.
The isoclonal signature is taken from 18 h trajectories
of 400 cells.
Fig. 3. Episodic-bursty expression dominates across the human genome. (A)
To create the polyclonal population, cells are infected with a lentiviral vector
expressing d2GFP so that each cell represents a unique clone harboring a sin-
gle semirandom integration of reporter. (B) Resultant noise maps for over
8,000 individual cell trajectories for the HIV-1 LTR promoter, EF1A promoter,
and UBC promoter. The constitutive origin is derived from Fig. 2D(18 h).
17456 Dar et al.
delayed switching to the transcriptional ON state in a two-state
transcription model (17) and predicts that noise frequency (not
only noise magnitude) (27) is modulated in different genomic or
chromatin environments (SI Appendix). In agreement with this
prediction, the distribution of points in the LTR noise map indi-
cates significant differences from the UBC and EF1A noise maps.
In further support, an alternate representation of the noise-map
distribution that converts the distributions to centroids (with
error bars) can be used to conveniently visualize these differences
between promoters (SI Appendix, Fig. S4) (11).
Given conflicting reports on whether burst frequency varies
with genomic location (1719, 28), we next determined if tran-
scriptional burst size, burst frequency, or both changed across the
genome (Fig. 4A). As mentioned above, transcriptional bursting
can be quantified by a two-state model of transcription (13, 14) in
which switching between the two states occurs at rates kon and
koff , and transcription only occurs in the ON state with a rate
km(Eqs. 13and Materials and Methods). The burst size, or num-
ber of mRNAs generated per activity pulse, is typically defined
as kmkoff and, in the limit of koff kon, the burst frequency
is defined as kon (Fig. 4A) (17). To directly test if transcriptional
burst frequency changes across genome location, we analyzed the
polyclonal three-dimensional noise-space data to fit values for
km,koff , and kon. LTR polyclonal trajectories are subclustered
into groupings of approximately 60 cells, so that each subcluster
represents cells in a specific range of gene-expression levels (SI
Appendix, Fig. S5), and average noise autocorrelation is calcu-
lated for each subcluster by autocorrelation analysis (12). To
validate this subclustering approach, we verified that CV2values
from subclustered polyclonal trajectories agree with CV2from
conventional flow cytometry data from isoclonal populations
(Compare Fig. 4Cto SI Appendix, Figs. S1 and S6) . Thus, each
polyclonal subcluster corresponds to an isoclonal population in
terms of average expression level (SI Appendix, Fig. S6). Strik-
ingly, this genome-wide data demonstrate that autocorrelation
time first increases with increasing expression and once an ex-
pression threshold is reached (gray line, Fig. 4B), autocorrelation
time decreases as expression level increases (Fig. 4B). This pat-
tern of concavity is inconsistent with constant burst frequency
(i.e., constant kon) across genomic locations and provides a
genome-wide measurement of kon and koff changes that is inde-
pendent of km(10).
Conventional approaches to quantify transcriptional burst
kinetics analyze noise magnitude (CV2-versus-hGFPi) (6, 7) and
the polyclonal data from Fig. 3 can also be analyzed in terms of
noise magnitude on the CV2-versus-hGFPiplane of noise space
(Fig. 4C). This noise magnitude analysis shows a strong initial
decrease in CV2at low expression levels (Fig. 4C). Then when
an expression threshold is reached (gray line, Fig. 4C) a leveling
off of CV2is observed for higher expression levels (Fig. 4C).
However, noise magnitude is insufficient to uniquely parameter-
ize the two-state model. Because burst size couples kmand koff ,
it is only through the τ12measurement, which is not influenced
by km, that the two parameters can be differentiated from one
another. Note, that the gray line in Fig. 4 Band Ccorrespond to
the same expression threshold.
Fitting of the two-state model in the polyclonal three-dimen-
sional noise map space shows a strong initial increase in burst
frequency at low expression levels, whereas burst size remains
almost constant (Fig. 4 Dand E). Upon reaching a threshold
expression level (gray vertical line in Fig. 4D), a switch in burst
dynamics occurs, and burst size increases, whereas burst fre-
quency remains constant (Fig. 4 Dand E). The fold change in
transcriptional burst size and burst frequency values reveals that
both vary equally across genomic loci (Fig. 4Fand SI Appendix,
Fig. S6). In addition, the measured burst-size range predicts an
average mRNA level of 110 molecules per cell, which is consis-
tent with previous measurements that used single-molecule
mRNA fluorescent in situ hybridization (29, 30). The success of
fitting the three-dimensional noise space is reflected in the close
agreement between a simulated autocorrelation curve and the
experimental trend (Fig. 4B) with the fit model parameters. This
fit shows that the assumed two-state model is sufficient to de-
scribe the measured system (SI Appendix). Much like LTR,
UBC, and EF1A display similar fold changes in burst size and
frequency and exhibit a similar pattern of increasing burst fre-
quency, followed by increasing burst size (Fig. 4G). These data
indicate that integration site influences burst kinetics, irrespective
of promoter type (i.e., cis sequence). However, UBC and EF1A
exhibit almost constant τ12at the highest expression levels indi-
cating increases in only kmat these levels (SI Appendix, Fig. S7).
Interestingly, these two strong promoters individually span the
range of burst frequencies recently reported for a variety of
mammalian genes (18) (SI Appendix, Fig. S7), whereas the LTR
functions at much lower burst frequencies (Fig. 4).
To test how transcriptional activators influence burst dynamics
(Fig. 4), transcription was perturbed with transcriptional
activators, including the histone deacetylase inhibitor (HDAC)
trichostatin A (TSA), and the cell-signaling molecule tumor ne-
crosis factor α(TNF). TNF enhances expression by stimulating
Fig. 4. Transcriptional burst frequency and burst size vary equally across
the genome and are strongly dependent on expression level. (A) Schematic
of the two-state model of transcriptional bursting, where the promoter
switches between ON and OFF states at rates kon and koff and transcribes at
rate kmin the ON state. Transcriptional dynamics are modulated through
changes in burst size, burst frequency, or both. (B) Noise autocorrelation,
noise magnitude (C), burst frequency (D), and burst size (E) versus abundance
for polyclonal subclusters of 2,000 12-h Ld2G single-cell trajectories. Low and
high abundance domains are separated by a solid gray threshold line which
indicates the changes in the trends of noise autocorrelation, noise magni-
tude, and hence burst size and burst frequency is observed. (Fand G)Asa
function of hGFPi, fold changes in burst size and frequency are comparable,
with an initial increase of frequency in all promoters investigated.
Dar et al. PNAS October 23, 2012 vol. 109 no. 43 17457
recruitment of a p50-RelA heterodimer to nuclear factor κB
(NFκB) binding sites (31) and the HIV-1 LTR encodes multiple
NFκB binding sites and is potently activated by TNF (22). We
previously reported that TNF only changes burst frequency of
the LTR while conserving burst size in a limited number of
isoclones (17), and were interested to see how widespread this
phenomena was across the genome. The τ12-versus-hGFPiana-
lysis of TNF stimulation shows a significant decrease in τ12with
increasing expression level (Fig. 5A). Here, τ12decrease with
increasing abundance is a direct indication of kinetic changes
and demonstrates that increasing expression level cannot be
explained solely by modulations of km(SI Appendix, Eq. S1)
(10, 13).
Fitting of the different two-dimensional planes of three-
dimensional noise space upon TNF induction (Fig. 5 Aand B)
demonstrates that both burst frequency and size significantly
increase as expression levels increase, with burst frequency
increasing at low expression levels and burst size increasing at
higher expression levels (Fig. 5 Cand D). Interestingly, there
appears to be a threshold in expression level, above which kon
plateaus to values observed before adding TNF, and koff appears
to decrease. These data and analysis are corroborated by con-
ventional flow cytometry measurements of 35 isoclonal popula-
tions (SI Appendix, Fig. S6). Overall, these results suggest that
TNF induces expression from the LTR along existing burst trends
(Fig. 5Cand D), and the use of TSA, which induces expression
through a different mechanism than TNF (32), corroborates this
observation (SI Appendix). This observed decrease in koff with
TNF induction leads to extended duration of bursts and is con-
sistent with the reported inhibition of p50-HDAC1 repressive-
complex formation at LTR NFκB sites by p50/RelA heterodimers
(33)the successful formation of HDAC1 leads to weakened
recruitment of RNA polymerase II and weakened transcriptional
initiation (34). The observed increases in kon are also consistent
with increased recruitment of RNA polymerase II to the LTR
promoter NFκB sites induced by TNF (35, 36). Fitted parameter
estimates of LTR residency time in the presence of TNF were
used to represent an average over the first 12 h of stimulation
given the dynamic nonlinear nature of the NFκBresponse(37,
38). Collectively, these results enable estimation of LTR residency
time in the transcriptional ON and OFF states and show that TNF
extends duration in the ON state up to eightfold (Fig. 5E).
The analysis of noise space presented here provides a high-through-
put method to dynamically profile gene-regulatory mechanisms
and the effects of perturbations on gene expression. A significant
methodological advantage of analyzing three dimensions of noise
space is the ability to more accurately constrain two-state transcrip-
tional burst models and the polyclonal nature of the approach en-
ables shotgun mapping of gene regulation dynamics on a genome-
wide scale.
The resulting genome-wide data demonstrate that constitutive
transcription is rare across the human genome. Instead, the
overwhelming majority of human genomic loci appear to stochas-
tically fire in episodic bursts. Analysis of noise space demon-
strated that both transcriptional burst frequency and burst size
vary in roughly equal degree across the human genome
(Fig. 4 DG). Intriguingly, there appears to be a threshold expres-
sion level below which integrations modulate only transcriptional
burst frequency and above which only burst size is modulated
(Fig. 4 BGand SI Appendix, Figs. S6 and S7). This transition
could result from recently reported refractory periods inherent to
bursting kinetics (18, 39). Burst frequency can be increased at
loci where transcriptional bursts are infrequent, but as frequency
increases, the refractory period temporally precludes further
increases in frequency. Therefore, once this frequency ceiling is
reached, the only way to increase expression is to increase the
transcription rate or extend the duration of each burst.
As proposed (40), widespread episodic bursting may allow
limited transcriptional resources within the cell to be efficiently
allocated to achieve high-level transcription across large numbers
of loci. This efficient allocation of resources may be the biological
analog of time-domain multiplexing approaches used to effi-
ciently transmit data in signal processing applications.
Materials and Methods
Lentiviral Vectors. Lentiviral vectors were cloned as described (41) and used to
infect 5×105Jurkat cells at a multiplicity of infection <0.1, resulting in
25,00050,000 infected cells each with a unique integration site. Cells were
then sorted by FACS and fluorescently imaged on glass-bottom dishes in RPMI
medium 1640 with 10% fetal calf serum and 1% penicillin-streptomycin.
Imaging. Imaging was performed in humidified conditions at 37 °C and 5%
CO2for 1224 h with a 40X (1.2 N.A.) oil-immersion objective on an Olympus
DSU microscope equipped with an automated linear-encoded X-Y stage, as
described in refs. (12) and (42). Image processing and cell tracking were
performed in Matlab with an in-house algorithm (12) and a single 12-h
experiment could generate up to 1,000 trajectories for analysis.
Calculations. For each trajectory, noise autocorrelation (τ12) and noise mag-
nitude (CV2) were calculat ed using an established noise-processing algorithm
(11, 12). A reported theory (10, 13) of the two-state transcriptional bursting
model yields analytical expressions for both the autocorrelation of the noise,
τ12, and the noise magnitude, CV2(see SI Appendix, Materials and Methods).
Detailed discussion of the noise-mapping approach, including analytical
arguments and stochastic simulations (of Poissonian gene expression) by
the Gillespie algorithm (43, 44), are described in detail in SI Appendix.
Transcriptional burst dynamics are quantified by deriving analytical ex-
pressions for burst size and burst frequency with formulations from previous
analyses (13, 14, 17) and low promoter activity assumptions where koff kon,
koff km,koff γpand kmðγmþγpÞ):
hpi;hpBF ·BS ·kp
or b[1]
Fig. 5. Transcriptional burst size and frequency are altered by transcrip-
tional activators. (AD) TNF addition (filled red circles) shifts the measured
integration sites to the higher abundance and burst dynamic domain along
the nondrug curve (empty circles). Large autocorrelation shifts implicate
changes in burst kinetics. (E) Estimated residence times in the active (ON)
and inactive (OFF) states.
17458 Dar et al.
BS ¼CV2·hpi
BF ¼hpi
BS ·C1·C2
where BS is the burst size, BF is the burst frequency, kmis the transcription
rate, kpis the translation rate, γmand γpare the mRNA and protein decay
rates, respectively, hPiis the mean protein abundance, and bis the transla-
tional burst rate. hPi, or the mean number of GFP molecules in the measure-
ments, is assumed to be directly proportional to hFLi, the mean fluorescence
intensity. Eqs. 2and 3reveal that measurements of CV2and hFLiare sufficient
to quantify burst size and burst frequency within a constant, which is only
dependent on the translation rate and decay rates of mRNA and protein.
Assuming these remain constant, while varying integration site or promoter
sequence, an abundance-dependent burst size and frequency trend can be
directly resolved.
Note. Full decomposition of koff,kon , and kmcan only be performed through
the use of the full 3D noise space.
ACKNOWLEDGMENTS. We thank Hana El-Samad, Ido Golding, Jim Kadonaga,
Laurie Boyer, Alex Hoffmann, John Cooke, David Karig, and members of the
Weinberger and Simpson labs for helpful comments. B.S.R. was supported by
National Science Foundation Graduate Research Fellowship Grant 1144247
and by National Institutes of Health (NIH) Molecular Biophysics Training
Grant GM08326. L.S.W. acknowledges support from the Pew Scholars Pro-
gram in the Biomedical Sciences and the Alfred P. Sloan Research Fellowship
Program. This work was supported by the NIH Directors New Innovator
Award (OD006677) (to L.S.W.), the in-house research program at the Center
for Nanophase Materials Sciences at Oak Ridge National Laboratory (spon-
sored by the Office of Basic Energy Sciences, US Department of Energy)
(to R.D.D. and M.L.S.), and by the National Institute of General Medical
Sciences National Systems Biology Centers at University of California at
San Diego (P50 GM085764) and University of California, San Francisco (P50
1. Taniguchi Y, et al. (2010) Quantifying
E. coli
proteome and transcriptome with single-
molecule sensitivity in single cells. Science 329:533538.
2. YungerS, Rosenfeld L, Garini Y, Shav-Tal Y (2010) Single-allele analysis of transcription
kinetics in living mammalian cells. Nat Methods 7:631633.
3. Golding I, Paulsson J, Zawilski SM, Cox EC (2005) Real-time kinetics of gene activity in
individual bacteria. Cell 123:10251036.
4. Pedraza JM, Paulsson J (2008) Effects of molecular memory and bursting on fluctua-
tions in gene expression. Science 319:339343.
5. Cai L, Friedman N, Xie XS (2006) Stochastic protein expression in individual cells at
the single molecule level. Nature 440:358362.
6. Newman JR, et al. (2006) Single-cell proteomic analysis of
S. cerevisiae
reveals the
architecture of biological noise. Nature 441:840846.
7. Bar-Even A, et al. (2006) Noise in protein expression scales with natural protein
abundance. Nat Genet 38:636643.
8. So LH, et al. (2011) General properties of transcriptional time series in
Escherichia coli
Nat Genet 43:554560.
9. Blake WJ, M KÆrn, Cantor CR, Collins JJ (2003) Noise in eukaryotic gene expression.
Nature 422:633637.
10. Cox CD, McCollum JM, Allen MS, Dar RD, Simpson ML (2008)Using noise to probe and
characterize gene circuits. Proc Natl Acad Sci USA 105:1080910814.
11. Austin DW, et al. (2006) Gene network shaping of inherent noise spectra. Nature
12. Weinberger LS, Dar RD, Simpson ML (2008) Transient-mediated fate determination in
a transcriptional circuit of HIV. Nat Genet 40:466470.
13. Simpson ML, Cox CD, Sayler GS (2004) Frequency domain chemical Langevin analysis of
stochasticity in gene transcriptional regulation. J Theor Biol 229:383394.
14. Kepler TB, Elston TC (2001) Stochasticity in transcriptional regulation: Origins,
consequences, and mathematical representations. Biophys J 81:31163136.
15. Sigal A, et al. (2006) Variability and memory of protein levels in human cells. Nature
16. Cohen AA, et al. (2008) Dynamic proteomics of individual cancer cells in response to a
drug. Science 322:15111516.
17. Singh A, Razooky B, Cox CD, Simpson ML, Weinberger LS (2010) Transcriptional
bursting from the HIV-1 promoter is a significant source of stochastic noise in HIV-1
gene expression. Biophys J 98:L32L34.
18. Suter DM, et al. (2011) Mammalian genes are transcribed with widely different
bursting kinetics. Science 332:472474.
19. Skupsky R, Burnett JC, Foley JE, Schaffer DV, Arkin AP (2010) HIV promoter integration
site primarily modulates transcriptional burst size rather than frequency.PLoS Comput
Biol 6:e1000952.
20. Mitchell RS, et al. (2004) Retroviral DNA integration: ASLV, HIV, and MLV show distinct
target site preferences. PLoS Biol 2:11271137.
21. Schroder AR, et al. (2002) HIV-1 integration in the human genome favors active genes
and local hotspots. Cell 110:521529.
22. Jordan A, Defechereux P, Verdin E (2001) The site of HIV-1 integration in the human
genome determines basal transcriptional activity and response to Tat transactivation.
EMBO J 20:17261738.
23. Kim DW, UetsukiT, Kaziro Y, Yamaguchi N, Sugano S (1990) Use of the human elonga-
tion factor 1 alpha promoter as a versatile and efficient expression system. Gene
24. Ramezani A, Hawley TS, Hawley RG (2000) Lentiviral vectors for enhanced gene
expression in human hematopoietic cells. Mol Ther 2:458469.
25. Harper CV, et al. (2011) Dynamic analysis of stochastic transcription cycles. PLoS Biol
26. Kao SY, Calman AF, Luciw PA, Peterlin BM (1987) Anti-termination of transcription
within the long terminal repeat of HIV-1 by tat gene product. Nature 330:489493.
27. Sakane N, et al. (2011) Activation of HIV transcription by the viral Tat protein requires
a demethylation step mediated by lysine-specific demethylase 1 (LSD1/KDM1). PLoS
Pathog 7:e1002184.
28. Lo MY, Rival-Gervier S, Pasceri P, Ellis J (2012) Rapid transcriptional pulsing dynamics
of high expressing retroviral transgenes in embryonic stem cells. PLoS One 7:e37130.
29. Raj A, Peskin CS, Tranchina D, Vargas DY, Tyagi S (2006) Stochastic mRNA synthesis in
mammalian cells. PLoS Biol 4:17071719.
30. Singh A, Razooky B, Dar RD, Weinberger LS (2012) Dynamics of protein noise can
distinguish between alternative sources of gene-expression variability. Mol Syst Biol
31. Vallabhapurapu S, Karin M(2009) Regulation and function of NF-kappaB transcription
factors in the immune system. Annu Rev Immunol 27:693733.
32. VanLint C, Emiliani S, Ott M, Verdin E (1996) Transcriptional activation and chromatin
remodeling of the HIV-1 promoter in response to histone acetylation. EMBO J
33. Hayden MS, Ghosh S (2004) Signaling to NF-kappaB. Genes Dev 18:21952224.
34. Williams SA, et al. (2006) NF-kappaB p50 promotes HIV latency through HDAC recruit-
ment and repression of transcriptional initiation. EMBO J 25:139149.
35. West MJ, Lowe AD, Karn J (2001) Activation of human immunodeficiency virus
transcription in T cells revisited: NF-kappaB p65 stimulates transcriptional elongation.
J Virol 75:85248537.
36. Barboric M, Nissen RM, Kanazawa S, Jabrane-Ferrat N, Peterlin BM (2001) NF-kappaB
binds P-TEFb to stimulate transcriptional elongation by RNA polymerase II. Mol Cell
37. Williams SA, Kwon H, Chen LF, Greene WC (2007) Sustained induction of NF-kappa B
is required for efficient expression of latent human immunodeficiency virus type 1.
J Virol 81:60436056.
38. Hoffmann A, Levchenko A, Scott ML, Baltimore D (2002) The IkappaB-NF-kappaB
signaling module: Temporal control and selective gene activation. Science
39. Suter DM, Molina N, Naef F, Schibler U (2011) Origins and consequences of transcrip-
tional discontinuity. Curr Opin Cell Biol 23:657662.
40. Cai L, Dalal CK, Elowitz MB (2008) Frequency-modulated nuclear localization bursts
coordinate gene regulation. Nature 455:485490.
41. Weinberger LS, Burnett JC, Toettcher JE, Arkin AP, Schaffer DV (2005) Stochastic
gene expression in a lentiviral positive-feedback loop: HIV-1 Tat fluctuations drive
phenotypic diversity. Cell 122:169182.
42. Weinberger LS, Shenk T (2007) An HIV feedback resistor: Auto-regulatory circuit
deactivator and noise buffer. PLoS Biol 5:6781.
43. Gillespie DT (1976) General method for numerically simulating stochastic time evolu-
tion of coupled chemical-reactions. J Comput Phys 22:403434.
44. McCollum JM, Peterson GD, Cox CD, Simpson ML, Samatova NF (2006) The sorting
direct method for stochastic simulation of biochemical systems with varying reaction
execution behavior. Comput Biol Chem 30:3949.
Dar et al. PNAS October 23, 2012 vol. 109 no. 43 17459
... These metastable states lasted for days and were proposed to be analogous to acute and latent states in the cells of patients [63,70]. The existence of a basal noise from the HIV-1 promoter was suggested by an earlier study [97] and further supported by time-lapse observations of single cells infected with LTR-GFP vectors without the Tat feedback loop, which displayed fluctuations in GFP level around the mean [16,[98][99][100]. Overall, these studies demonstrated the key importance of noise in HIV-1 latency control. ...
... The experiments described above provided arguments that the HIV-1 promoter f tions in a noisy manner, producing transcriptional bursts [16,[98][99][100]. However, t studies used GFP or destabilized GFP as reporters, with 20 h and 2 h half-lives, res tively, and they required the viral RNA to be spliced, exported and translated. ...
... The experiments described above provided arguments that the HIV-1 promoter functions in a noisy manner, producing transcriptional bursts [16,[98][99][100]. However, these studies used GFP or destabilized GFP as reporters, with 20 h and 2 h half-lives, respectively, and they required the viral RNA to be spliced, exported and translated. ...
Full-text available
This review summarizes current advances in the role of transcriptional stochasticity in HIV-1 latency, which were possible in a large part due to the development of single-cell approaches. HIV-1 transcription proceeds in bursts of RNA production, which stem from the stochastic switching of the viral promoter between ON and OFF states. This switching is caused by random binding dynamics of transcription factors and nucleosomes to the viral promoter and occurs at several time scales from minutes to hours. Transcriptional bursts are mainly controlled by the core transcription factors TBP, SP1 and NF-κb, the chromatin status of the viral promoter and RNA polymerase II pausing. In particular, spontaneous variability in the promoter chromatin creates heterogeneity in the response to activators such as TNF-α, which is then amplified by the Tat feedback loop to generate high and low viral transcriptional states. This phenomenon is likely at the basis of the partial and stochastic response of latent T cells from HIV-1 patients to latency-reversing agents, which is a barrier for the development of shock-and-kill strategies of viral eradication. A detailed understanding of the transcriptional stochasticity of HIV-1 and the possibility to precisely model this phenomenon will be important assets to develop more effective therapeutic strategies.
... For each SNP from each spot, we plotted the fraction of reads mapping to the major allele versus the total coverage. When coverage is low, one would expect a broader spread in allele frequencies, due to random sampling biases and transcriptional bursts [37], and this is indeed what we observed. At higher coverage, read ratios tended to stabilize at one-to-one ratios mapping to the major/minor alleles. ...
Full-text available
Spatial transcriptomic technologies, such as the Visium platform, measure gene expression in different regions of tissues. Here, we describe new software, STmut, to visualize somatic point mutations, allelic imbalance, and copy number alterations in Visium data. STmut is tested on fresh-frozen Visium data, formalin-fixed paraffin-embedded (FFPE) Visium data, and tumors with and without matching DNA sequencing data. Copy number is inferred on all conditions, but the chemistry of the FFPE platform does not permit analyses of single nucleotide variants. Taken together, we propose solutions to add the genetic dimension to spatial transcriptomic data and describe the limitations of different datatypes.
... We reasoned that, for this type of approach to be viable, we needed a genetic circuit that promotes gene expression variability. As "burst-like" transcriptional activation is known to lead to wide variance in expression levels (54)(55)(56)(57), we reasoned that a switchlike inducible gene expression system, such as with rtTA and DOX, set to intermediate activator expression levels could generate a wide variety of OCT4 trajectories. Via deterministic modeling, we considered the impact of DNA copy number, set by the multiplicity of infection (MOI), on the variability of the OCT4 distribution generated by the inducible trajectory generator in Fig. 3A and an analogous system in which the inducible system is replaced with a constitutive promoter. ...
Full-text available
Reprogramming human fibroblasts to induced pluripotent stem cells (iPSCs) is inefficient, with heterogeneity among transcription factor (TF) trajectories driving divergent cell states. Nevertheless, the impact of TF dynamics on reprogramming efficiency remains uncharted. We develop a system that accurately reports OCT4 protein levels in live cells and use it to reveal the trajectories of OCT4 in successful reprogramming. Our system comprises a synthetic genetic circuit that leverages noise to generate a wide range of OCT4 trajectories and a microRNA targeting endogenous OCT4 to set total cellular OCT4 protein levels. By fusing OCT4 to a fluorescent protein, we are able to track OCT4 trajectories with clonal resolution via live-cell imaging. We discover that a supraphysiological, stable OCT4 level is required, but not sufficient, for efficient iPSC colony formation. Our synthetic genetic circuit design and high-throughput live-imaging pipeline are generalizable for investigating TF dynamics for other cell fate programming applications.
... A very debated topic is the occurrence of dropouts, which can occur due to various factors like incomplete reads, amplification errors or even transcriptional bursts (Dar et al., 2012). Through modeling and zero inflation, dropouts can be imputed or artifacts removed. ...
Full-text available
Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.
... In this section, we focus on the modes of HIV-1 transcription during latency and reactivation. Transcriptional bursting, which refers to the intermittent production of bursts of transcripts [97], is a characteristic feature of HIV-1 transcription [92,98], and is a major source of gene expression heterogeneity [99][100][101]. Transcriptional bursting appear to be regulated by a plethora of molecular factors, including TF regulation [102,103], chromatin environment [101], nucleosome positioning [104,105], and regulation of promoter-proximal RNAPII pausing [106,107] and recycling [108]. ...
Full-text available
HIV-1 latency is a major barrier to curing infections with antiretroviral therapy and, consequently, to eliminating the disease globally. The establishment, maintenance, and potential clearance of latent infection are complex dynamic processes and can be best described with the help of mathematical models followed by experimental validation. Here, we review the use of viral dynamics models for HIV-1, with a focus on applications to the latent reservoir. Such models have been used to explain the multi-phasic decay of viral load during antiretroviral therapy, the early seeding of the latent reservoir during acute infection and the limited inflow during treatment, the dynamics of viral blips, and the phenomenon of post-treatment control. Finally, we discuss that mathematical models have been used to predict the efficacy of potential HIV-1 cure strategies, such as latency-reversing agents, early treatment initiation, or gene therapies, and to provide guidance for designing trials of these novel interventions.
In recent years anatomical pathology has been revolutionised by the incorporation of molecular findings into routine diagnostic practice, and in some diseases the presence of specific molecular alterations are now essential for diagnosis. Spatial transcriptomics describes a group of technologies that provide up to transcriptome‐wide expression profiling while preserving the spatial origin of the data, with many of these technologies able to provide these data using a single tissue section. Spatial transcriptomics allows expression profiling of highly specific areas within a tissue section potentially to subcellular resolution, and allows correlation of expression data with morphology, tissue type and location relative to other structures. While largely still research laboratory‐based, several spatial transcriptomics methods have now achieved compatibility with formalin‐fixed paraffin‐embedded tissue (FFPE), allowing their use in diagnostic tissue samples, and with further development potentially leading to their incorporation in routine anatomical pathology practice. This mini review provides an overview of spatial transcriptomics methods, with an emphasis on platforms compatible with FFPE tissue, approaches to assess the data and potential applications in anatomical pathology practice.
Gene transcription often occurs in discrete bursts, and it can be difficult to deduce the underlying regulatory mechanisms for transcriptional bursting with limited experimental data. Here, we categorize numerous states of single eukaryotic genes and identify 6 essential transcriptional events, each comprising a series of state transitions; transcriptional bursting is characterized as a sequence of 4 events, capable of being organized in various configurations, in addition to the beginning and ending events. By associating transcriptional kinetics with mean durations and recurrence probabilities of the events, we unravel how transcriptional bursting is modulated by various regulators including transcription factors. Through analytical derivation and numerical simulation, this study reveals key state transitions contributing to transcriptional sensitivity and specificity, typical characteristics of burst profiles, global constraints on intrinsic transcriptional noise, major regulatory modes in individual genes and across the genome, and requirements for fast gene induction upon stimulation. It is illustrated how biochemical reactions on different time scales are modulated to separately shape the durations and ordering of the events. Our results suggest that transcriptional patterns are essentially controlled by a shared set of transcriptional events occurring under specific promoter architectures and regulatory modes, the number of which is actually limited.
The serial nature of reactions involved in the RNA life-cycle motivates the incorporation of delays in models of transcriptional dynamics. The models couple a transcriptional process to a fairly general set of delayed monomolecular reactions with no feedback. We provide numerical strategies for calculating the RNA copy number distributions induced by these models, and solve several systems with splicing, degradation, and catalysis. An analysis of single-cell and single-nucleus RNA sequencing data using these models reveals that the kinetics of nuclear export do not appear to require invocation of a non-Markovian waiting time.
Coordinated expression of ion channels is crucial for cardiac rhythms, neural signaling, and cell cycle progression. Perturbation of this balance results in many disorders including cardiac arrhythmias. Prior work revealed association of mRNAs encoding cardiac Na V 1.5 ( SCN5A ) and hERG1 ( KCNH2 ), but the functional significance of this association was not established. Here, we provide a more comprehensive picture of KCNH2 , SCN5A , CACNA1C , and KCNQ1 transcripts collectively copurifying with nascent hERG1, Na V 1.5, Ca V 1.2, or KCNQ1 channel proteins. Single-molecule fluorescence in situ hybridization (smFISH) combined with immunofluorescence reveals that the channel proteins are synthesized predominantly as heterotypic pairs from discrete molecules of mRNA, not as larger cotranslational complexes. Puromycin disrupted colocalization of mRNA with its encoded protein, as expected, but remarkably also pairwise mRNA association, suggesting that transcript association relies on intact translational machinery or the presence of the nascent protein. Targeted depletion of KCHN2 by specific shRNA resulted in concomitant reduction of all associated mRNAs, with a corresponding reduction in the encoded channel currents. This co-knockdown effect, originally described for KCNH2 and SCN5A , thus appears to be a general phenomenon among transcripts encoding functionally related proteins. In multielectrode array recordings, proarrhythmic behavior arose when I Kr was reduced by the selective blocker dofetilide at IC 50 concentrations, but not when equivalent reductions were mediated by shRNA, suggesting that co-knockdown mitigates proarrhythmic behavior expected from the selective reduction of a single channel species. We propose that coordinated, cotranslational association of functionally related ion channel mRNAs confers electrical stability by co-regulating complementary ion channels in macromolecular complexes.
Full-text available
The transcription factor NF-kappaB has been the focus of intense investigation for nearly two decades. Over this period, considerable progress has been made in determining the function and regulation of NF-kappaB, although there are nuances in this important signaling pathway that still remain to be understood. The challenge now is to reconcile the regulatory complexity in this pathway with the complexity of responses in which NF-kappaB family members play important roles. In this review, we provide an overview of established NF-kappaB signaling pathways with focus on the current state of research into the mechanisms that regulate IKK activation and NF-kappaB transcriptional activity.
Full-text available
Within individual cells, two molecular processes have been implicated as sources of noise in gene expression: (i) Poisson fluctuations in mRNA abundance arising from random birth and death of individual mRNA transcripts or (ii) promoter fluctuations arising from stochastic promoter transitions between different transcriptional states. Steady-state measurements of variance in protein levels are insufficient to discriminate between these two mechanisms, and mRNA single-molecule fluorescence in situ hybridization (smFISH) is challenging when cellular mRNA concentrations are high. Here, we present a perturbation method that discriminates mRNA birth/death fluctuations from promoter fluctuations by measuring transient changes in protein variance and that can operate in the regime of high molecular numbers. Conceptually, the method exploits the fact that transcriptional blockage results in more rapid increases in protein variability when mRNA birth/death fluctuations dominate over promoter fluctuations. We experimentally demonstrate the utility of this perturbation approach in the HIV-1 model system. Our results support promoter fluctuations as the primary noise source in HIV-1 expression. This study illustrates a relatively simple method that complements mRNA smFISH hybridization and can be used with existing GFP-tagged libraries to include or exclude alternate sources of noise in gene expression.
Full-text available
Single cell imaging studies suggest that transcription is not continuous and occurs as discrete pulses of gene activity. To study mechanisms by which retroviral transgenes can transcribe to high levels, we used the MS2 system to visualize transcriptional dynamics of high expressing proviral integration sites in embryonic stem (ES) cells. We established two ES cell lines each bearing a single copy, self-inactivating retroviral vector with a strong ubiquitous human EF1α gene promoter directing expression of mRFP fused to an MS2-stem-loop array. Transfection of MS2-EGFP generated EGFP focal dots bound to the mRFP-MS2 stem loop mRNA. These transcription foci colocalized with the transgene integration site detected by immunoFISH. Live tracking of single cells for 20 minutes detected EGFP focal dots that displayed frequent and rapid fluctuations in transcription over periods as short as 25 seconds. Similarly rapid fluctuations were detected from focal doublet signals that colocalized with replicated proviral integration sites by immunoFISH, consistent with transcriptional pulses from sister chromatids. We concluded that retroviral transgenes experience rapid transcriptional pulses in clonal ES cell lines that exhibit high level expression. These events are directed by a constitutive housekeeping gene promoter and may provide precedence for rapid transcriptional pulsing at endogenous genes in mammalian stem cells.
Full-text available
The essential transactivator function of the HIV Tat protein is regulated by multiple posttranslational modifications. Although individual modifications are well characterized, their crosstalk and dynamics of occurrence during the HIV transcription cycle remain unclear. We examine interactions between two critical modifications within the RNA-binding domain of Tat: monomethylation of lysine 51 (K51) mediated by Set7/9/KMT7, an early event in the Tat transactivation cycle that strengthens the interaction of Tat with TAR RNA, and acetylation of lysine 50 (K50) mediated by p300/KAT3B, a later process that dissociates the complex formed by Tat, TAR RNA and the cyclin T1 subunit of the positive transcription elongation factor b (P-TEFb). We find K51 monomethylation inhibited in synthetic Tat peptides carrying an acetyl group at K50 while acetylation can occur in methylated peptides, albeit at a reduced rate. To examine whether Tat is subject to sequential monomethylation and acetylation in cells, we performed mass spectrometry on immunoprecipitated Tat proteins and generated new modification-specific Tat antibodies against monomethylated/acetylated Tat. No bimodified Tat protein was detected in cells pointing to a demethylation step during the Tat transactivation cycle. We identify lysine-specific demethylase 1 (LSD1/KDM1) as a Tat K51-specific demethylase, which is required for the activation of HIV transcription in latently infected T cells. LSD1/KDM1 and its cofactor CoREST associates with the HIV promoter in vivo and activate Tat transcriptional activity in a K51-dependent manner. In addition, small hairpin RNAs directed against LSD1/KDM1 or inhibition of its activity with the monoamine oxidase inhibitor phenelzine suppresses the activation of HIV transcription in latently infected T cells. Our data support the model that a LSD1/KDM1/CoREST complex, normally known as a transcriptional suppressor, acts as a novel activator of HIV transcription through demethylation of K51 in Tat. Small molecule inhibitors of LSD1/KDM1 show therapeutic promise by enforcing HIV latency in infected T cells.
Full-text available
In individual mammalian cells the expression of some genes such as prolactin is highly variable over time and has been suggested to occur in stochastic pulses. To investigate the origins of this behavior and to understand its functional relevance, we quantitatively analyzed this variability using new mathematical tools that allowed us to reconstruct dynamic transcription rates of different reporter genes controlled by identical promoters in the same living cell. Quantitative microscopic analysis of two reporter genes, firefly luciferase and destabilized EGFP, was used to analyze the dynamics of prolactin promoter-directed gene expression in living individual clonal and primary pituitary cells over periods of up to 25 h. We quantified the time-dependence and cyclicity of the transcription pulses and estimated the length and variation of active and inactive transcription phases. We showed an average cycle period of approximately 11 h and demonstrated that while the measured time distribution of active phases agreed with commonly accepted models of transcription, the inactive phases were differently distributed and showed strong memory, with a refractory period of transcriptional inactivation close to 3 h. Cycles in transcription occurred at two distinct prolactin-promoter controlled reporter genes in the same individual clonal or primary cells. However, the timing of the cycles was independent and out-of-phase. For the first time, we have analyzed transcription dynamics from two equivalent loci in real-time in single cells. In unstimulated conditions, cells showed independent transcription dynamics at each locus. A key result from these analyses was the evidence for a minimum refractory period in the inactive-phase of transcription. The response to acute signals and the result of manipulation of histone acetylation was consistent with the hypothesis that this refractory period corresponded to a phase of chromatin remodeling which significantly increased the cyclicity. Stochastically timed bursts of transcription in an apparently random subset of cells in a tissue may thus produce an overall coordinated but heterogeneous phenotype capable of acute responses to stimuli.
To stimulate transcriptional elongation of HIV-1 genes, the transactivator Tat recruits the positive transcription elongation factor b (P-TEFb) to the initiating RNA polymerase II (RNAPII). We found that the activation of transcription by RelA also depends on P-TEFb. Similar to Tat, RelA activated transcription when tethered to RNA. Moreover, TNF-α triggered the recruitment of P-TEFb to the NF-κB-regulated IL-8 gene. While the formation of the transcription preinitiation complex (PIC) remained unaffected, DRB, an inhibitor of P-TEFb, prevented RNAPII from elongating on the IL-8 gene. Remarkably, DRB inhibition sensitized cells to TNF-α-induced apoptosis. Thus, NF-κB requires P-TEFb to stimulate the elongation of transcription and P-TEFb plays an unexpected role in regulating apoptosis.
An exact method is presented for numerically calculating, within the framework of the stochastic formulation of chemical kinetics, the time evolution of any spatially homogeneous mixture of molecular species which interreact through a specified set of coupled chemical reaction channels. The method is a compact, computer-oriented, Monte Carlo simulation procedure. It should be particularly useful for modeling the transient behavior of well-mixed gas-phase systems in which many molecular species participate in many highly coupled chemical reactions. For “ordinary” chemical systems in which fluctuations and correlations play no significant role, the method stands as an alternative to the traditional procedure of numerically solving the deterministic reaction rate equations. For nonlinear systems near chemical instabilities, where fluctuations and correlations may invalidate the deterministic equations, the method constitutes an efficient way of numerically examining the predictions of the stochastic master equation. Although fully equivalent to the spatially homogeneous master equation, the numerical simulation algorithm presented here is more directly based on a newly defined entity called “the reaction probability density function.” The purpose of this article is to describe the mechanics of the simulation algorithm, and to establish in a rigorous, a priori manner its physical and mathematical validity; numerical applications to specific chemical systems will be presented in subsequent publications.
In both prokaryotes and eukaryotes, transcription has been described as being temporally discontinuous, most genes being active mainly during short activity windows interspersed by silent periods. In mammalian cells, recent studies performed at the single cell level have revealed that transcriptional kinetics are highly gene-specific and constrained by the presence of refractory periods of inactivity before a gene can be turned on again. While the underlying mechanisms generating gene-specific kinetic characteristics remain unclear, various biological consequences of transcriptional discontinuity have been unravelled during the past few years. Here we review recent advances on understanding transcriptional kinetics of individual genes at the single cell level and discuss its possible origins and consequences.