Parts Characterizations

The characterization of newly constructed biological parts as described below.

List of characterizations

• rtTA3/TRE promoter
• Gal4/UAS promoter
• LacI/Hef1a-LacO repressor
• Gal4→LacI¬rtTA3→2-input AND gate
• CMV-TetO promoter
• Mnt-VP16/Mnt promoter
• CI434-VP16/CI434 promoter

rtTA3/TRE promoter

Explanation:

The rtTA3 system relies on two elements: the rtTA3 gene which encodes an activator and the TRE promoter which rtTA3 binds to. Upon doxcycline binding to rtTA3, it is able to to bind to TRE and activate expression of downstream genes. In this particular experiment, we transfected Hek293 cells with 266 ng each of Hef1a:eBFP2, Hef1a:rtTA3, and TRE:delta-mCherry constructs. Cells were then induced with varying levels of doxycycline for 48 hours before FACS. Because we generally observe >50% transfection efficiency with Lipofectamine in Hek293 cells, we took only the top 50% of cells by TxRed signal for the displayed data. We display the mean TxRed intensity for the top 50% of events verus doxycycline concentration. Control cells were given the same set of DNA but without Hef1a:rtTA3.

We can clearly observe a steep increase in red signal which levels off at dox levels above 0.5 ug/mL. Meanwhile the control cells remain at baseline, showing that doxycyline itself does not affect fluoresence levels.

Gal4/UAS promoter

Explanation:

Another activator promoter pair we make use of is the Gal4-UAS system. Gal4VP16 is an activator capable of binding to UAS promoter site and activating expression of downstream genes. Here we characterize this interaction with a set of transfections. Cells were transfected with UAS:(mKate, eYFP-FF4, eBFP2, or H2B-citrine) and Hef1a:GV16. Controls lack the Hef1a:GV16 plasmid. The above graph displays mean fluoresence in the denoted channels.

As can be clearly seen, Hef1a:GV16 results in significant activation of the UAS promoter. This results in the observed increases in mean fluoresence compared to control populations. With the exception of UAS:eBFP2, we see more than 20-fold increase in mean fluorescence.

LacI/Hef1a-LacO repressor

Explanation:

In order to implement inhibition into our circuit designs, we made use of the lacO/lacI system. lacI binds a lacO site and can inhibit transcription of downstream genes. This inhibition can be relieved by binding lacI with IPTG. In order to test this part, we constructed a Hef1a-lacO promoter which is normally constitutively active until inhibition by lacI.

As can be seen in the figure above, we see significant inhibition of eYFP expression with addition of a Hef1a:lacI construct (around 20 fold decrease). Finally, as we increase IPTG levels, we see mean yellow fluorescence increase as lacI inhibition is relieved. We also incorporate our UAS/Gal4 system by putting lacI expression under control of UAS, thus giving us a different constitutive level of lacI production. Again we observe inhibition of lacO by lacI relieved by IPTG.

Gal4-VP16→LacI¬rtTA3→2-input AND gate

Explanation:

Here we show an experimental result of an example of what we call our internal processing module, which is essentially a 2 input chemical AND gate. Taking the individual functional transcription factor systems characterized above, we combine them into an AND gate with tewo inputs: IPTG and doxycycline levels. The system integrates the UAS/GV16, rtTA3/TRE, and lacI/lacO systems described above. IPTG is required to relieve inhibition of rtTA3 expression. rtTA3 then needs doxycycline to activate TRE. Therefore, we require both IPTG and dox to achieve expression of our final Delta-mCherry reporter.

The data show the desired 2 input AND behavior. We see maximum Delta-mCherry expression at the highest levels of both dox and IPTG. We also observe significant activation at high dox levels even in the absence of IPTG. This suggests some leakiness in the lacI inhibition, allowing for some rtTA3 to be produced and activated by dox./p>

The above result, obtained from flow cytometry by measuring the mCherry fluorescence of transfected Hek293 cells in the presence of different IPTG and doxycycline concentrations, are as we expected. In the absence of doxycycline, rtTA3 cannot activated the TRE promoter, and with increasing IPTG concentration, expressed LacI cannot inhibit the Hef1a-LacO promoter, and so we see a rise in Delta-mCherry due to this.

CMV-TetO promoter

Explanation:

The Elowitz lab has generously provided us with their stable cell lines used to characterize Notch Delta in their paper: Cis-interactions between Notch and Delta generate mutually exclusive signalling states. The cells were CHO-TRex cells stably transfected with CMV-TO:Delta-mCherry. In the absence of doxycycline, CMV-TO is inhibited by TetR. Upon doxycline addition, this inhibition is relieved. We made use of this inducible delta expression to act as "senders". In order to characterize this doxycline induction, we treated these cells with varying levels of dox and measured red fluorescence.

We display mean TxRed signal from flow cytometry versus dox levels. We clearly observe a sharp increase in signal upon dox addition which levels out.

Mnt-VP16/minCMV-4xMnt promoter

Explanation:

Mnt is similar in role to Gal4-VP16. It binds to Mnt sites and can activate expression of downstream genes. We made a minCMV-4xMnt promoter upstream of the mKate gene. Mnt presence should activate expression of mKate and thus an increase in red fluorescence. We transfected Hek293 cells with Hef1a:rtTA3, TRE:Mnt-VP16, and minCMV-4xMnt:mKate.

From the data shown, we can clearly see an increase in mean red fluorescence upon dox addition

CI434-VP16/minCMV-1xCI434 promoter

Explanation:

CI434 is another transcriptional activator that we characterized. In order to test it, we transfected Hek293 cells with Hef1a:rtTA3, TRE:CI434-VP16, and minCMV-1xCI434:mKate. This allowed us to use dox induction to activate expression of CI434 which in turn should activate mKate expression.

The results above show a clear increase in red fluorescent signal upon dox addition. We observe around a 1.25 fold increase. We believe this is an underestimate of the actual activation strength due to lack of gating and low transfection efficiency for this experiment.

Patterning Modeling Results

All simulations were done using our modeling framework, mcell. For more information on mcell, please look in the Tools section. The raw data for a large number of runs of various models can be downloaded from here; videos are not included to save on space, but can be rendered using the mcell framework.

Most of our modeling was done on the following circuit:

All cells contain this circuit; the values of all proteins are 0 at start.

We followed the model of Delta-Notch interaction proposed by Elowitz[1]. In it, Delta and Notch in the same cell cis-inhibit at a rate proportional to their amounts; Delta in one cell can bind to Notch in another cell (at a rate proportional to the available Notch and Delta), destroying both proteins and releasing the Notch Intracellular Domain (NICD), which then goes on bind to a promoter and stimulated production of genes.

Applying this model, along with standard mass action to model protein binding events and Hill equations to model promoter stimulation (along with the standard set of simplifying assumptions that go along with these methods), we arrive at the following series of differential equations:

d[NCis]/dt = n_prod - k_cis*[NCis]*[DCis] - k_trans*[NCis]*[DTrans]
d[DCis]/dt = d_min + d_prod/(1+[LacI]*b_L) - k_cis*[NCis]*[DCis] - k_trans*[NTrans]*[DCis]
d[NICD]/dt = k_trans*[NCis]*[DTrans]
d[Reporter]/dt = d[NCAD]/dt = d[LacI]/dt = V_max*(Gal4^h)/(k_d+Gal4^h)

DTrans is the amount of Delta in neighboring cells touching the current cell; likewise, NTrans is the amount of Notch in neighboring cells touching the current cell. We assumed that Delta and Notch are all located on the cell surface and are evenly distributed. NCis is the amount of Notch inside the given cell; DCis is the amount of Delta inside the given cell. The values for V_max, h, and k_d were taken from [1]; for the simulations shown on this page, n_prod = 5, d_min = 1, d_prod = 9 (so that the Delta production rate varies between 1 and 10); k_cis = 1, k_trans = 0.04, and b_L = 10.

Bimodality in HEK cells

Running this circuit in HEK cells (modeled by setting the energy of contact between cells in CompuCell3D to -10.0 and initializing the cells with no gaps) produces stunning bimodality.

Video

Histogram Movie

Analysis

What happens here is that, initially, all the cells develop a large amount of Delta. However, some Notch still remains in the cells, and is strongly activated; Delta production rates begin to drop because of the LacI. Some cells gain significant amounts of Notch before others, due to the random movements of the cells. Once a cell gains a significant amount of Notch, it's Delta level drops off sharply, and it ceases to activate cells in its vincinity. Thus, once a cell is fully "activated", other cells around it are much less likely to be. The stable state of such dynamics is a mosaic pattern, in which single cells are activated and are surrounded by a sea of inactivated cells.

But what if they are not sticky?

There is a very large discrepancy between the behavior of the circuit in HEK cells in CHO cells, which are modeled with no adhesion and are initialized with gaps in between them. In CHO cells, the following ensues:

Movie

Histograms

Analysis

Bimodality almost completely drops off in this model. This is clear from the video; looking at the histograms, we can see that only an extremely small hump develops at the end in place of where the hump for the activated mode should be. The cells drift around randomly, most of the far away from others and thus inactivated; sometimes they bump in to one another, and a cell or two begins the road to activation, but they never stay together for long enough for the dominance of the activated cell to be established and for bimodality to be enforced.

Adding in NCAD

Now, we add in the NCad. Using the AdhesionFlex plugin, we set the adhesion strength of NCad to 1 (modifying this within reasonable bounds does not really change things much) and set the amount of NCad in CompuCell3D to equal the amount of Reporter in our BionetSolver circuit. This created the following results:

Movie

Note: this is a long video; the behavior of the cells never really settles down into a stable state.

Histograms

Analysis

This circuit never really settles down into any clear behavior. Looking at the histograms, we can see that there is a fairly large gap between most "activated" and "non-activated" cells, but the "activated" cells have a much wider range of Reporter values and the range drifts over time. Looking at the video, one can see certain cells that end up always being unactivated, certain cells that are always activated, and certain cells with a moderate Reporter level that oscillates in a complicated fashion. What seems to be happening is that as a cell randomly becomes activated, it causes other cells to stick to it; this creates enough cells in close proximity to one another to cause more cells to activate, which weakens the aggregate structure, causing the cells to start drifting apart. Analysis of this model is ongoing. We tried clustering the cells, but standard algorithms did not do a very good job in clustering cells into groups that share the same behavior. This may be because cells seem to change behaviors -- a cell that looks like it is always off can, in the middle of the simulation, become an always on cell.

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[1] Sprinzak, et al. Cis-interactions between Notch and Delta generate mutually exclusive signalling states. Nature 465:86-90

Patterning Experimental Results

One of the key modules in our self-organizing cell porject is intercellular communication. We achieved this through the use of the Nothc/Delta signalling system. As described under the Project page, Notch/Delta is a cell-cell contact based signalling system with Delta acting as the sending ligand and Notch as the receiver. The Notch we used was a fusion of the Notch extracellular domain with an intracellular Gal4 domain. Upon Delta binding, the intracellular domain is cleaved and Gal4 can migrate to the nucleus and activate UAS. In the following experiments, we tested out this interaction by working with the stably integrated cell lines provided by Elowitz, as well as cells transfected with our own constructs based off plasmids also from Elowtiz.

In the following experiment, we decided to get preliminary data on Notch-Delta interaction. We co-cultured Sender and Receiver type CHO cells in a similar experiment to the one presented in Elowtiz's paper. The genotypes of the cell types used can be found below: ----

TO-DMC (Sender) cells

CHO T-REx stably integrated with: pcDNA5/TO‐Deltamcherry

Purpose: These cells normally inhibit expression of Delta-mCherry. However, doxycyline can relieve this repression and start expression of Delta. These cells can only send using their Delta ligand, so we termed them "senders".

hN1G4esn‐No‐Delta (Receiver Type)

CHO T-REx stably integrated with: CMV-Cerulean, UAS-H2B-Citrine, pcDNA3‐hNECD‐Gal4esn

Purpose: These cells express Notch constitutively and have a Histone-Citrine fusion under UAS control as the readout. We termed these cells as receivers.

hN1G4esn (Cis-inhibitor Type)

CHO T-REx stably integrated with: CMV-Cerulean, UAS-H2B-Citrine, pcDNA3‐hNECD‐Gal4esn, pcDNA5/TO‐Deltamcherry

Purpose: These cells express Notch constitutively and inhibit Delta expression in the absence of doxycycline. In this state, they are effectively receiver type. However, as dox levels increase, we see more and more Delta expression and due to cis-inhibition, we see a switch to sender state. Because these cells allow us to observe and modulate cis-inhibition, we termed them "cis-inhibitor type".

Delta-Notch interaction

Explanation:

We tested Notch Delta signalling by recreating the co-culture experiment Elowitz performed by mixing Sender and Receiver cells in varying ratios. We then added doxycycline to the samples to begin Delta expression, which should begin to activate Notch and thus expression of Citrine. We also mixed receiver cells with blank CHO cells as a negative control. As can be observed in the microscopy images above, addition of 2 ug/mL of dox significantly increased red signal from the sender cells and correspondingly an increase in nuclear yellow fluorescence in the reciever cells. We observe this increase in red and yellow fluorescence in both the 50:50 and 80:20 ratios.

We then put these cells through flow cytometry to observe overall population fluorescence. The above data was obtained by taking the mean FITC signal of the top 50% of the 50:50 ratio and 20% of the 80:20. We observe a significant increase in mean FITC signal in the Sender+Receiver population compared to the controls (no dox and blank cells). This data suggests successful Notch Delta activation using Elowitz's cell lines.

Explanation:

Next, we decided to test our own Notch Delta constructs by performing a similar experiment as above. We transfected our Hef1a:Delta-mCherrry construct into Hek293 cells and performed a similar co-culture experiment. We mixed these Hek sender cells with Elowitz receiver CHO cells in the specified ratios. The results of flow cytometry are displayed above.

We again took the top 50 or 20% of FITC cells and took their average FITC signal. Again, we can see an increase in fluorescence in the test samples compared to the controls. This suggests that our Delta construct is also capable of signalling to Notch.

Mammalian Cell Adhesion

Cadherins are a medically important calcium-dependent surface protein that can bind to same-type cadherins on other cells. The existence of different types of cadherins allows for the development of highly organized tissues, as the differential expression of different types of cadherins in various cells drives the development of complex three-dimensional structures.

We're in the process of incorporating cadherins into our existing characterized signaling work, with the goal of achieving control of cadherin expression in mammalian cells. By integrating cadherins into our pattern-formation mechanism, we'll be able to create self-adhesive patterns that could serve as a building platform for a novel approach to regenerative medicine and tissue engineering, revolutionizing organogensis.

Attributions

Our instructors were very helpful not only in giving feedback on our designs, cloning strategies, and data, but also in training us for lab work. The training for tissue culture work was conducted by Linda Stockdale in the Griffiths lab. Gibson assembly techniques and FACS training from Deepak Mishra, one of our instructors. Jonathan Babb, another one of our instructors helped us with setting up the robot.

Other than the initial training, all programming, DNA, and transfection work was done by our undergraduate team.

Special credit belongs to Semon Rezchikov for simulations and modeling, Jenny Cheng for animations, and Tiffany Huang for wiki design.

Special thanks to Michael Elowitz for generously providing not only the CMV:hN1ECD-Gal4esn, CMV-TO:Delta-mCherry, and UAS:H2B-Citrine constructs, but also the stably integrated cell lines. We could not have implemented the Notch-Delta system without these valuable contributions.