Time-lapse transcription. Even genetically identical cells (far left) can have widely varying levels of
gene expression. This variability arises from fluctuations in gene expression in a single cell over time
(first box). These fluctuations can result from random activation and inactivation of the gene itself
(white rectangle, center box), potentially involving
the sequences in the gene’s promoter (black arrow).
Ultimately, the stochastic behavior of the molecules
responsible for transcription, such as RNA polymerase II (Poll II, right box), must be responsible
for gene-level variability.
whether a gene is competent for the initiation of transcription can lead to “bursts”
in transcription (4–6). Suter et al. show
that individual genes have unique bursting
dynamics, and they begin to explore the biochemistry that underlies this phenomenon.
To conduct their study, Suter et al. created a number of populations of mouse
fibroblasts, each of which had a particular gene engineered to express a readily
observed bioluminescent protein in place
of its usual product. They found that genes
can show large temporal fluctuations in the
synthesis of the protein. These can only be
explained if transcription takes place in a
burst-like rather than continuous fashion.
By using careful mathematical data modeling, they determined the rates of gene activation, RNA production when active, and
gene inactivation. Each individual gene they
studied exhibited markedly different values
for all of these parameters. This shows that
there are no “global” parameters that are
inherent to the transcriptional process (such
as a global rate of transcription when active).
In an effort to begin answering the ques-
tions of whether and how gene expression
dynamics are controlled, Suter et al. used
artificial promoters to study expression. They
found that increasing the number of binding
sites increased the RNA burst size and, to a
lesser extent, the rate of gene reactivation,
but did not affect gene deactivation. This sug-
gests that the biochemical steps that control
deactivation and initiation are not the same as
those that control reactivation, and echoes the
results of earlier ensemble distribution mea-
surements (5, 7). Going further, Suter et al.
used a model-based technique to reconstruct
the most likely time sequences for changes in
the gene state and RNA transcript numbers
for each cell. Their analysis of the distribu-
tion of the time periods when genes are active
and inactive strongly suggests that gene inac-
tivation is controlled primarily by a single
rate-limiting step. In contrast, reactivation
requires the completion of several sequential
or parallel processes with comparable rates.
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