Effects of resource sharing on
gene expression dynamics
Patrick M. Caveney1,2
S. Elizabeth Norred1,2
Charles W. Chin1,2
Jonathan B. Boreyko1,2,3
Brandon S. Razooky2,4
Scott T. Retterer1,2,5
C. Patrick Collier2
Michael L. Simpson1,2,6
1.
2.
3.
4.
5.
6.
Bredesen Center, University of Tennessee, Knoxville
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory
Department of Biomedical Engineering and Mechanics, Virginia Tech
Laboratory of Immune Cell Epigenetics and Signaling, The Rockefeller
University
Biosciences Division, Oak Ridge National Laboratory
Joint Institute for Biological Sciences, University of Tennessee, Knoxville and
Oak Ridge National Laboratory
Introduction
Method
Results
Episodic gene expression, bursting, is defined by periods of high
expression, ON, separated by periods of no expression, OFF, and has
been observed in all kingdoms of life[1]. When a gene is ON it draws
heavily from a shared pool of reusable resources (e.g. polymerase,
ribosomes) for a short time while using no resources during OFF
periods. Bursting is characterized by burst size (i.e. duration of a burst)
and burst frequency (i.e. number of bursts per time). Resource sharing
leads to interactions between genes with no regulatory relationships[2].
We use a synthetic, microfabricated platform to investigate the effect of
sharing limited, reusable resources on burst size and frequency.
Lithographically defined, actuatable, PDMS chambers (height of ~5 µm
and diameters of 10, 5, and 2 µm with volumes 393, 99, and 16 fL)
contained an EGFP expressing cell-free protein synthesis system[3].
Reactions were also confined in POPC water-in-water vesicles prepared
by oil emulsion[4] with volumes ranging from ~20 fL to ~2000 fL. The
amount of protein in each chamber was measured for one hour. The
deterministic trend of EGFP growth was removed to isolate the noise
of protein expression in each chamber. Full details in[3].
The key finding is that larger shared resource pools prefered to produce
protein by extending existing bursts (i.e. increasing burst size) instead of
initiating more bursts (i.e. increasing burst frequency). This result was
confirmed in two different systems of cell-sized reactions, PDMS
chambers and POPC vesicles. Simulations of many genes pulling
resources from a common pool indicated increased burst size in larger
reactions was responsible for increased protein abundance. This
happened because the first few mRNA created gathered the largest
portion of resources, regardless of the reaction size, and in larger
reactions there were more resources available to gather. Thus, in
agreement with in vivo experiments[5],
protein abundance is
preferentially modulated by burst size not burst frequency.
How is bursting affected by resource sharing?
Figure 1: A) Bursty gene
expression is caused by genes
transitioning between ON and
OFF states. While in the ON
state the genes draw heavily
from a common pool of
resources. B) We experimentally
vary the way resources are
shared, either by increasing the
number of discrete pools or
increasing the size of one pool.
C) We observe how protein
abundance may increases either
by increased burst frequency or
increased burst size. D) Changes
in burst size and frequency can
be distinguished on a noise plot
of CV2 vs protein abundance[3].
A
15 min
30 min
60 min
Figure 4: (left) CV2 vs. protein abundance for 2, 5, and 10 μm diameter chambers. The
small filled data points represent individual chambers while the large filled data points
show the mean behaviors for all chambers of a given size. The dotted line is of the form
a/Abundance and where a is a constant that is calibrated so the line passes through the
mean of the 2 μm diameter chambers (large filled orange triangle). The open orange
triangles show combinations of 2 μm chambers which share resources in the same manner
as single 2 μm chambers. The leftmost open triangle shows the average behavior of sums
of 2 individual 2 μm chambers, while the rightmost open triangle shows the average
behavior of sums of 6 individual 2 μm chambers. CV2 for these combinations of 2 μm
chambers closely follow the a/Abundance trend showing individual 2 μm chambers were
statistically independent and burst frequency increased with increasing abundance while
burst size was constant. In contrast, the individual 5 μm chambers deviate strongly (solid
line) from the a/Abundance trend even though their volume and protein abundance were
about equal to six 2 μm chambers (red box). The 5 μm chambers had proportionally larger
resource pools, and shared these resources by increasing burst size, not burst frequency.
(right) CV2 vs. protein abundance for vesicles ranging in diameter from 4 μm to 19 μm.
Each data point (gray or colored) represents an individual vesicle. The orange points are
vesicles with diameters 9-10 μm, and the blue points have diameters 18-19 μm. The solid
line is a power law fit to all points. While abundance varies by 3 orders of magnitude, CV 2
only decrease by about 1 order of magnitude. Dashed lines show fits to individual volumes
(orange and blue) where CV2 goes as 1/Abundance2.
Gillespie simulation of resource sharing
120 min
135 min
mRNA made early gather disproportionate amounts of resources
B
EGFP production in cell-free reactions confined to cell-sized
PDMS chambers and POPC vesicles was observed
0 min
Figure 3. Protein abundance after 1
hour scaled linearly with volume in
both the chambers (left) and
vesicles (right). The volume range
of the vesicles was larger than the
chambers (orange band).
Increasing protein abundance is driven by increased burst size in
PDMS chambers and POPC vesicles
C
D
Protein abundance scales linearly with volume
150 min
180 min
Figure 2: Cell-free reactions were confined in both PDMS microfluidic chambers and
POPC vesicles of varying volumes. Reactions in both constitutively expressed EGFP, and
fluorescence increased over one hour. For both methods fluorescence was measured every
3 minutes. Noise was extracted by subtracting a general trend (gray line) scaled by a gain
factor from each individual trace[3]. The resulting noise traces were quantified with the
coefficient of variation squared (CV2 = variance / mean2) and plotted against fluorescence.
Figure 5: Schematic of Gillespie
simulation of resource sharing.
Genes bursted ON and OFF with
rates kON and kOFF, respectively.
While ON, genes produced mRNA
at rate . Ribosomes in a common
pool (i.e. reusable resource
molecules) associated with mRNA
with rate constant kb. The number
of genes varied between 5 and 50,
and the number of resources varied
proportionally with 300 resources
per gene.
Figure 6: (left) CV2 vs protein abundance from the model described in Figure 5. Colors
represent the size of the reaction from 5 to 50 genes. Large points are means of multiple
runs of the same reaction size. The solid line is a power law fit to all data points while the
dashed line is of the form a/Abundance2 where a is selected so that the line passes
through the mean of a 35 gene reaction. As in the experimental chambers and vesicle
data, CV2 from this model was relatively insensitive to increases in abundance driven by
changes in reaction sizes. This shows that between reaction sizes abundance increases
were driven by increased burst size. In contrast, CV2 is highly sensitive to increases in
abundance that occur within a single reaction size, predicting that within a single reaction
size increased abundance is the result of increased burst frequency and decreased burst
size. This behavior was also seen experimentally in the chambers and vesicles (Figure 3).
(right) mRNA molecules are ranked in the order of the time they were created. The yaxis shows the fraction of the total protein translated from each mRNA molecule. Points
are colored by the reaction size (small reaction sizes are more blue, larger ones are more
red). mRNA molecules made early, regardless of the reaction size, collected a
disproportionate amount of resources and made a disproportionate amount of the total
protein.
Acknowledgements
This research was conducted at the Center for Nanophase Materials Sciences,
which is sponsored at Oak Ridge National Laboratory by the Scientific User
Facilities Division, Office of Basic Energy Sciences, U.S. Department of Energy.
The authors would also like to thank the following individuals for their fruitful
discussions Jennifer Morrell-Falvey, Carmen Foster, and Roy Dar.
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