Research Project Description

Research Project Description
Jenna Noll
October 6, 2014
Salmonella enterica serovar Typhimurium cells swim through their environment using rotating flagella. The flagellum consists of a basal body, a hook, and a filament. The
expression of the flagellar genes is divided into a transcriptional heirarchy. The subdivision of the operons into 3 classes is based on the promoters that drive transcription.
The heirarchy allows genes needed earlier in construction to be transcribed before genes
that are needed later.
The class 1 genes flhC and flhD encode FlhC and FlhD proteins, which form a
transcriptional activation complex FlhD4 C2 . FlhD4 C2 is a cofactor to the sigma factor
σ 70 in facilitating transcription of class 2 promoters. Class 2 operons encode the hook
basal body (HBB) along with two regulatory genes, fliA and flgM. FliA is known as σ28
and is necessary to begin transcription of class 3 genes. σ28 recruits RNA polymerase to
class 3 promoters. Class 3 operons encode proteins for filament assembly and flagellar
function, and include the proteins that make up the filament and motor, along with
some chemosensory proteins.
It has been observed that within a population of Salmonella, there are distinct subpopulations of fliC -ON and fliC -OFF cells. The mechanism for this is not completely
clear. It is also observed that within a population there are varying numbers of flagella
per cell. There are many factors that we believe contribute to these variations. In particular, these include four operons that are known to be expressed from both class 2 and
class 3 promoters. These are the fliAZY, flgMN, fliDST, and flgKL operons.
σ28 , the transcription factor required for expression from class 3 promoters, is expressed from a hybrid class 2/3 promoter. Before completion of HBBs, FlgM binds to
σ28 and prevents it from activating class 3 promoters. FlgM is also expressed from both
class 2 and 3 promoters. When HBBs are completed and have switched to secreting flagellar type molecules, FlgM is secreted from the cell and σ28 is free to activate expession
from class 3 promoters.
FliT is a protein that is expressed from both class 2 and class 3 genes. Before
HBBs are complete, it is bound to another class 2/3 protein, FliD. Once HBBs are complete, FliD is secreted from the cell and this frees up FliT from complex. FliT interacts
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with FlhD4 C2 and prevents it from binding to DNA, thereby inhibiting production from
class 2 promoters, and effectively shutting down new flagellar construction. When FliT
is bound to FlhD4 C2 , it promotes degredation of FlhD4 C2 through ClpX.
YdiV is a non-flagellar protein that is involved in flagellar gene regulation. YdiV is
known to bind to FlhD4 C2 . When YdiV is bound, it promotes degredation of FlhD4 C2
through ClpX. It also prevents FlhD4 C2 from binding to DNA to activate class 2 promoters, and also encourages unbinding of FlhD4 C2 from DNA. YdiV is thought to regulate construction of flagella in response to nutritional cues. When Salmonella enterica
serovar Typhimurium are in poor nutrition, it has been observed that there is more
YdiV around. YdiV inhibits FlhD4 C2 from activating class 2 gene expression. In good
nutrition, there is less inhibition of FlhD4 C2 by YdiV.
FliZ is also expressed from a hybrid class 2/3 promoter. FliZ upregulates the FlhD4 C2
indirectly by inhibiting YdiV. FliZ interacts with the ydiV promoter and prevents transcription of YdiV. In this way, FliZ participates in two positive feedback loops, since by
upregulating FlhD4 C2 , there is an increase in both class 2 and class 3 promoter activity.
We have studied some preliminary modeling of the regulatory networks mentioned
above. We believe that while these models are a good way to begin thinking about
regulation, these networks are an inappropriate way of modelling this system. All of the
models we have seen have been continuous, but we think that stochastics are the key to
explaining the variations within populations. There are many places in the system that
stochastics could be included. This includes in the modelling of HBBs, since only whole
complete HBBs secrete flagellar type proteins, and since there are only a few HBBs per
cell. This also includes in the binding of FlhD4 C2 to DNA. FlhD4 C2 is either bound or
not bound to DNA, and when it is not bound, class 2 genes are not being transcribed.
We plan to write stochastic models that could reproduce the data on variations in fliC
expression and flagellar number.
A sketch of our regulatory network can be found below. F represents FlhD4 C2 and H
represents HBBs. The labeling for everything else should be clear. We assume that σ28
and FlgM produced from class 2 genes bind tightly and immediately. We make a similar
assumption for FliT and FliD. Based on our network, in poor nutrition, YdiV would
inhibit FlhD4 C2 from starting transcription of class 2 genes. This would downregulate
the amount of FliZ in the system, leaving YdiV uninhibited and able to further repress
FlhD4 C2 . In rich nutrition, less YdiV would mean that FlhD4 C2 started to activate
class 2 promoters. In this case, class 2 genes would turn on FliZ to further inhibit YdiV.
Once HBBs were complete, the cell would secrete both FlgM and FliD, freeing up σ28
to turn on class 3 promoters and FliT to inhibit FlhD4 C2 from producing more HBBs.
This would limit the amount of flagella in the cell.
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F
F:FliT
F:YdiV
ClpX
ClpX
ydiV
F:DNA(C2)
FliT
YdiV
FliT:FliD
H
σ 28 :FlgM
FliZ
σ 28 (C3)
FliC
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A working model is as follows:
dF
dt
dPC2
dt
dM
dt
dC3
dt
dCF Y
dt
dY
dt
dZ
dt
dHM
dt
dHP
dt
dCDT
dt
dCF T
dt
dCT
dt
H
= βf − γf F − aF Y F Y + dF Y CF Y − aT f T F + dT F CT F
Y
)
kY + Y
kM HM
βC3 kC3 C3
= βC2 PC2 +
−
1 + K C3 C 3 K M + M
kM HM
=
− γC3 C3
KM + M
= (1 − PC2 )βC2 F − PC2 (γP C2 +
= aF Y − dF Y CF Y − γCF Y CF Y − γ ClpX CF Y
βY
− γY Y − aF Y F Y + dF Y CF Y + γ ClpX CF Y
1 + KZ Z
βC3 kC3 C3
= βC2 PC2 +
− γZ Z
1 + K C3 C 3
=
(1)
(2)
(3)
(4)
(5)
(6)
(7)
= βC2 PC2 − γHM HM − kHM
(8)
= kHM
(9)
= βC2 PC2 +
kCDT HCDT
βC3 kC3 C3
−
1 + KC3 C3 KCDT + CDT
= aF T F T − dF T CF T − γ ClpX CF T
kCDT HCDT
− aF T F T + dF T CF T − γT T + γ ClpX CF T
KCDT + CDT
= [HP ]
=
(10)
(11)
(12)
(13)
In our simulations we have that k=0 when HM starts building up. When HM reaches
a critical level, k switches to 1 so HP starts being produced. Then, when HP reaches an
integer amount, HBB increases by 1 and k switches back to zero.
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References
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[8] Takeo Wada, Tomoe Morizane, Tatsuhiko Abo, Akira Tominaga, Kanako InoueTanaka, and Kazuhiro Kutsukake. Eal domain protein ydiv acts as an anti-flhd4c2
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[9] Takeo Wada, Yasushi Tanabe, and Kazuhiro Kutsukake. Fliz acts as a repressor of the
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