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UCSF iGEM 2014 Community Response Individual Response ALPHA FACTOR DOXYCYCLINE RESPONSE SECONDARY SIGNAL COMMUNICATION RESPONSE STIMULUS

Cells in a local population have a wide range of responses to a given stimulus. This variation in cell response may be due to differences in immediate extracellular environment or intracellular molecular makeup. Nevertheless, in spite of local differences in cellular or environmental conditions, cellular systems often hinge on their component cells’ ability to coordinate and respond in a concerted way. For example, all T cells in a lymph node respond to an antigen, yet only those with a strong initial reaction proliferate and activate an immune response [1].

This phenomenon is also exhibited in quorum sensing by the bioluminescent bacteria V. fischeri. Through symbiosis with the Hawaiian Bobcat Squid, V. fischeri live in light organs at high cell densities. In these high densities, the V. fischeri bacteria can change the concentrations of certain chemicals in their local environment and activate bioluminescence in the entire bacterial population.

In some cases, the decision reaches consensus, and all cells respond the same, a phenomenon seen in quorum sensing by V. fischeri, for example, where gene expression is coordinated based on signals sensed at particular cell densities. In this example, cell populations somehow overcome the intrinsic biological noise found in all cellular populations and are able to respond in a concerted fashion. Alternatively, decisions can also favor the success of one individual in the group, namely the behavior of T cells in the human immune system. Again, intrinsic biological noise is suppressed as a coordinated and specific response is delivered by the population.

  • 1. Höfer, T., Krichevsky, O., & Altan-Bonnet, G. (2012). Competition for IL-2 between Regulatory and Effector T Cells to Chisel Immune Responses. Frontiers in Immunology, 3(September), 268. doi:10.3389/fimmu.2012.00268

Circuit Development

Conceptual Framework

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Conceptual Framework: Stimulus

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Conceptual Framework: Autonomous Response

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Conceptual Framework: Communication Response

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Conceptual Framework: Secondary Signal

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Conceptual Framework: Community Integration

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Conceptual Framework: Community Response

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Implementation

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Implementation: Doxycycline

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Implementation: rtTA

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Implementation: GFP

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Implementation: Mating Factor Alpha

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Implementation: Yeast Population Integration

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Implementation: RFP

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Conceptual Framework
Stimulus
Autonomous Response
Communication Response
Secondary Signal
Community Integration
Community Response
Implementation
Doxycycline
rtTA
GFP
Mating Factor Alpha
Yeast Population Integration
RFP

Promoter Characterization>

We have characterized two types of promoters endogenous to S. Cerevisiae; eleven alpha-factor responsive promoters (AFRP) and five constitutive promoters. To characterize these promoters, we cloned them into a shuttle vector plasmid to drive expression of GFP, which was subsequently integrated into yeast to create stable strains. Each construct’s fluorescence was measured using flow cytometry.

Alpha Factor Responsive Promoters (AFRP)

GFPAFRPMating Factor Alpha

AFRPs were selected from a microarray study where Roberts et. al. searched for genes responsive to yeast MAPK pathways, including the alpha factor response pathway [1]. We picked the promoters of the genes that were shown to be up-regulated in the presence of alpha factor. For each AFRP, we measured its construct’s fluorescence over 7 concentrations of alpha factor [0nM, 0.5nM, 1nM, 10nM, 100nM, 1000nM, 3000nM] after 90 minutes of induction. Through multiple rounds of testing, we found pAGA1 to have the highest expression level and with very low basal expression and pPCL2 to have the highest basal expression and the second highest induced expression.

Constitutive Promoters

GFPpTEF1

Constitutive promoters were selected from a study where Nevoigt et. al. used error prone PCR to create 14,000 pTEF1 mutants [2]. The study then selected 11 mutants with a wide range of promoter activity for further characterization. Out of these 11, we chose 5 of these mutants to use in our circuit. We confirmed that constitutive promoter pTEF1 m6 had the highest expression of GFP whereas constitutive promoter m7 had the lowest expression of GFP. Our data closely correlates with the results of the Nevoigt study.

Characterization data for all of our promoters can be found in Biobricks Submitted of our Achievements Section.

  • 1. Roberts, C. J. (2000). Signaling and Circuitry of Multiple MAPK Pathways Revealed by a Matrix of Global Gene Expression Profiles. Science, 287(5454), 873–880. doi:10.1126/science.287.5454.873
  • 2. Nevoigt, E., Kohnke, J., Fischer, C. R., Alper, H., Stahl, U., & Stephanopoulos, G. (2006). Engineering of promoter replacement cassettes for fine-tuning of gene expression in Saccharomyces cerevisiae. Applied and Environmental Microbiology, 72(8), 5266–73. doi:10.1128/AEM.00530-06

Results: Autonomous Circuit Verification

Doxycycline Dose Response of Different Autonomous Circuits

0 0.03 0.06 0.09 0.3 0.6 0.9 3 6 9 30 60 0 2 4 6 8 10 x 10 4 [Doxycycline](µg/µl) Mean GFP (AU) Control pTEF1 pTEF1(m3) pTEF1(m6) pTEF1(m7) pTEF1(m10)

Here, we demonstrate that the cell autonomous portion of our circuit behaves as designed. Each line represents a different version of the rtTA autonomous circuit as shown above. The circuits differ by the constitutive promoter driving rtTA expression; five mutants of pTEF1 were used to drive rtTA expression, creating a library of Doxycycline responsive circuits. In this graph, one can see that pTEF1(m7) driving rtTA results in the least sensitive circuit. At a doxycycline concentration of 0.3µg/µl, we achieve the greatest resolution between different cell autonomous circuits.

Results: Community Circuit Verification

Convergence of Community Response

Normalized GFP (A.U.) Normalized RFP (A.U.) Count Individual Response Community Response

In this figure, we show that we have successfully constructed a community averaging circuit in yeast. The blue histogram corresponds to yeast cells with pTEF1(m7) driving expression of rtTA, while the yellow histogram corresponds to yeast cells with pTEF1(m10) driving expression of rtTA. These two yeast lines where mixed and grown in the same well to produce a divergent population as shown in the divergent GFP peaks. This genetic divergence is averaged upon alpha factor signaling as shown in the overlapping RFP peaks.

Future Directions

To allow us to tune intercellular communication, we plan to add the following feedback loops to our base circuit above:

Positive Feedback: Mating Factor Alpha

MFα AFRP

In this positive feedback circuit, we express mating factor alpha under an alpha factor responsive promoter (AFRP). This generates a direct feedback loop where more alpha factor sensed effects more alpha factor secreted.

Positive Feedback: STE2

Ste2 AFRP

In this positive feedback circuit, we express the mating factor alpha receptor, STE2, under the control of an alpha factor responsive promoter. While yeast endogenously upregulate STE2 in response to alpha factor, our circuit would exacerbate that effect. Compared to the MFalpha positive feedback circuit (on the left), this feedback circuit is less direct as it upregulates a receptor that senses alpha factor. This does not change the local concentration of alpha factor, and may be thought of as a "selfish" feedback loop.

Negative Feedback: BAR1

Bar1 AFRP BAR1

In this negative feedback circuit, we express BAR1, an alpha factor degrading protease, under the control of an alpha factor responsive promoter. With BAR1, we expect to be able to decrease cellular noise and model divergent populations. Our computational models that demonstrate this expectation can be found in our Models section.

BioBricks Used and Submitted

AFRP Biobrick #
pASG7 BBa_K1346011
pPCL2 BBa_K1346013
pPRM1 BBa_K1346004
pPRM2 BBa_K1346005
pPRM3 BBa_K1346006
pPRM6 BBa_K1346007
pSAG1 BBa_K1346009
pCLG1 BBa_K1346010
pECM18 BBa_K1346008
pPRM6 BBa_K1346007
pHYM1 BBa_K1346012
pTEF1 Biobrick #
pTEF1 BBa_K431008
pTEF1.m3 BBa_K1346000
pTEF1.m6 BBa_K1346001
pTEF1.m7 BBa_K1346002
pTEF1.m10 BBa_K1346003

Promoter Activity By Alpha Factor Dose

0 0.5 1 10 100 1000 3000 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 [Alpha Factor](nM ) Mean GFP (AU )

Pheromone Response Factors: 2

Biobrick Specification

PRM2: Pheromone-regulated protein; transmembrane domains; regulated by Ste12p; required for efficient nuclear fusion

Promoter Activity By Alpha Factor Dose

0 0.5 1 10 100 1000 3000 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 [Alpha Factor](nM ) Mean GFP (AU )

Pheromone Response Factors: 2

Biobrick Specification

ASG7: Protein that regulates signaling from a G protein beta subunit Ste4p and its relocalization within the cell; specific to mating type a-cells and induced by alpha-factor

Promoter Activity By Alpha Factor Dose

0 0.5 1 10 100 1000 3000 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 [Alpha Factor](nM ) Mean GFP (AU )

Pheromone Response Factors: 4

Biobrick Specification

PCL2: Cyclin; interacts with cyclin-dependent kinase Pho85p; involved in the regulation of polarized growth and morphogenesis and progression through the cell cycle

Promoter Activity By Alpha Factor Dose

0 0.5 1 10 100 1000 3000 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 [Alpha Factor](nM ) Mean GFP (AU )

Pheromone Response Factors: 2

Biobrick Specification

CLG1: Cyclin-like protein that interacts with Pho85p; has sequence similarity to G1 cyclins PCL1 and PCL2

Promoter Activity By Alpha Factor Dose

Data Not Available :(

Pheromone Response Factors: 8

Biobrick Specification

HYM1: Component of the RAM signaling network that is involved in regulation cellular morphogenesis; localizes to sites of polarized growth during budding and during the mating response

Promoter Activity By Alpha Factor Dose

0 0.5 1 10 100 1000 3000 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 [Alpha Factor](nM ) Mean GFP (AU )

Pheromone Response Factors: 2

Biobrick Specification

PRM6: Pheromone-regulated protein; predicted to have 2 transmembrane segments; regulated by Ste12p during mating

Promoter Activity By Alpha Factor Dose

0 0.5 1 10 100 1000 3000 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 [Alpha Factor](nM ) Mean GFP (AU )

Pheromone Response Factors: 3

Biobrick Specification

PRM1: Pheromone-regulated multispanning membrane protein involved in membrane fusion during mating; localizes to the shmoo tip; regulated by Ste12p

Promoter Activity By Alpha Factor Dose

0 0.5 1 10 100 1000 3000 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 [Alpha Factor](nM ) Mean GFP (AU )

Pheromone Response Factors: 1

Biobrick Specification

ECM18: Protein of unknown function; similar to ribosomal-like protein 24

Promoter Activity By Alpha Factor Dose

0 0.5 1 10 100 1000 3000 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 [Alpha Factor](nM ) Mean GFP (AU )

Pheromone Response Factors: 3

Biobrick Specification

PRM3: Pheromone-regulated protein required for nuclear envelope fusion during karyogamy; localizes to the outer face of the nuclear membrane; interacts with Kar5p at the spindle pole body

Promoter Activity By Alpha Factor Dose

0 0.5 1 10 100 1000 3000 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 [Alpha Factor](nM ) Mean GFP (AU )

Pheromone Response Factors: 4

Biobrick Specification

SAG1: Alpha-agglutinin of alpha-cells; binds to Aga1p during agglutination

Promoter Activity By Alpha Factor Dose

0 0.5 1 10 100 1000 3000 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 [Alpha Factor](nM ) Mean GFP (AU )

Pheromone Response Factors: 2

Biobrick Specification

ORF of unknown function

Promoter Activity By Alpha Factor Dose

0 0.5 1 10 100 1000 3000 0 0.5 1 1.5 2 2.5 3 x 10 4 [Alpha Factor](nM ) Mean GFP (AU)

Pheromone Response Factors: 7

Biobrick Specification

AGA1: Anchorage subunit of a-agglutinin of a-cells;linked to adhesion subunit Aga2p via two disulfide bonds

pTEF1 Promoter Activity by Mutant

N/A pTEF1 m3 m6 m7 m1 0 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 x 10 4 pTEF1 Mutants Mean GFP (AU)

TEF1: Translational elongation factor EF-1; constitutive expression

pTEF1 Promoter Activity by Mutant

N/A pTEF1 m3 m6 m7 m1 0 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 x 10 4 pTEF1 Mutants Mean GFP (AU)

pTEF1 mutant by error-prone PCR

pTEF1 Promoter Activity by Mutant

N/A pTEF1 m3 m6 m7 m1 0 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 x 10 4 pTEF1 Mutants Mean GFP (AU)

pTEF1 mutant by error-prone PCR

pTEF1 Promoter Activity by Mutant

N/A pTEF1 m3 m6 m7 m1 0 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 x 10 4 pTEF1 Mutants Mean GFP (AU)

pTEF1 mutant by error-prone PCR

pTEF1 Promoter Activity by Mutant

N/A pTEF1 m3 m6 m7 m1 0 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 x 10 4 pTEF1 Mutants Mean GFP (AU)

pTEF1 mutant by error-prone PCR

Computational Models of Bar1 Negative Feedback

Part 1: Negative Feedback and Noise Reduction

Background: Noise can be defined as extracellular or intracellular perturbation which affects signal transduction as well as gene expression[1]. A signal pathway can possess mechanisms to reduce the influence of noise, especially for those that require high sensitivity, such as T cell receptors (TCR) [2]. The regulatory mechanisms downstream TCR include negative feedback which allows stimulation signals to be controlled, guaranteeing appropriate responses to external perturbations. Also, noise is regarded as a quantitative index of variability within a population, which can be shown by the distribution of population response when stimulated. We wonder if adding a negative feedback to the production of extracellular communication factor will reduce population noise.

Circuit Diagram: We have added the Bar1 gene, which is activated by alpha factor in the primary circuit [3]. Bar1 is a protease that cleaves alpha factor in the extracellular zone. So, alpha factor inhibits the accumulation of itself by producing Bar1, which forms a negative feedback for extracellular alpha factor. We can tune the strength of negative feedback with different alpha factor responsive promoters.

Modeling Result: Here is the data plot using stochastic model based on Chemical Langevin Equation [4]. The data along the whole time period is integrated into histograms on the right. From the results, we find that strains with negative feedback loop have lower noise (Upper panel), while strains without it have higher noise (Lower panel).

Part 2: Bimodal Response

Background: We explored another behavior pattern, bimodal response, which means when stimulated, the whole cell group differentiates into two subgroups, one of them activated while the other one remains in OFF state.

Circuit Design: This is the circuit we designed to achieve bimodal response. Based on the primary secrete-sense circuit, we add a positive feedback, and a degradation factor Bar1. The strength of positive feedback can be tuned by doxycycline concentration, and the strength of degradation factor can be tuned by changing pTEF promoter in front of Bar1, which we characterized experimentally.

Modeling Formula: In order to describe the dynamic of extracellular alpha factor concentration, we build a deterministic model based on this formula.

The first two terms represents generation of alpha factor: the first is the basal synthesis, and the second describes the positive feedback for generating alpha factor. We use sigmoidal form based on our pre-experiment results. The last two terms represents degradation of alpha factor. In this circuit, Bar1 is constitutively expressed, and the degradation of alpha factor by Bar1 is of first-order. The last term is natural degradation.

If we define this formula as the driving force, then the negative value of its integration can be defined as potential.

Modeling Results: Assuming the basal synthesis of alpha factor is zero, we plot the secretion term and degradation term against alpha concentration respectively.

Tuning the parameters to change the strength of positive feedback and degradation term, when extracellular alpha factor concentration stops changing at last, there are three fixed points – among which 2 are stable and 1 is unstable judged from Jacobian matrix. By calculating the potential (defined above), we correspond 2 of fixpoints to the 2 potential barriers where cells may gather. Thus a bimodal response appears.

Parameter Tuning: In this model, the strength of positive feedback changes by tuning V, while the strength of Bar1 degradation changes by tuning deltaBar1. So, we set a range for these two parameters to see under which condition bimodal response appears. This figure shows tuning deltaBar1, the slope of the degradation line. For different values, the pattern is distinctive. Along with its decrease, the potential difference between the barrier and of the well decreases, which means cells may have bigger probability to jump out of the well by stochastic events, thus bimodal pattern collapses.

This figure shows the results of tuning V, the highest value of secretion curve. Also, along with its decrease, the trend is similar with changing deltaBar1, resulting in difficulty maintaining bimodal response within cell population.

Kilobots

What are Kilobots?

Kilobots are small robots designed by the Self-Organizing Research Group at Harvard University to operate in large groups[5]. When programmed, the kilobots can cooperate through local interactions to perform collective behaviors such as swarming and self-assembly. They can flash LED lights, transmit and receive electronic signals to and from each other, and move using two vibration modules. For our purposes, we only need to use the LED light to report robot response.

Why model with Kilobots?

With their simple design, we hope to produce complex collective behavioral models that mimic our yeast circuits. By programming Kilobots to behave as our yeast cells, we hope to produce predicted community responses such as converging or diverging behaviors.

In our models, we utilized their ability to sense and secrete signals to produce community responses. A Kilobot can be in one of five states: NONE*, LOW, MED, HIGH, or MAX, which describe how frequently the cell transmits signals. A Kilobot’s state may vary depending on its program and the states of nearby Kilobots.

A Kilobot’s internal state is reported using its LEDs, which correspond to the following key:

  • None - Blue
  • Low - Cyan
  • Medium - White
  • High - Orange
  • Maximum - Red

*Kilobots in the NONE state send signals at a very low frequency; this is so that if isolated, the Kilobot will still be able to self-signal.

Circuit Components

  1. i_sense_rand_bar

    This code allows Kilobots (yeast cells) to sense and transmit electronic signals (mating factor alpha) that will influence other Kilobots’ states and may influence its own state (self-sensing). The Kilobots are randomized into different initial states, representing different initial constitutive expressions. There are five possible states, which describe the Kilobot’s output: NONE* [blue], LOW [cyan], MED [white], HIGH [orange], and MAX [red]. At each state, the cell lights up in the assigned color and transmits signals [value = 0] at the assigned frequency, NONE being the lowest level of frequency and MAX being highest. If a Kilobot senses enough signals transmitted by other Kilobots, it will go up in stage to the next highest level (so from NONE to LOW to MED to HIGH to MAX), until it reaches the MAX stage, at which point it will stay red. If the Kilobots sense enough signals transmitted by a i_bar1 Kilobot [value =1], it will go down a stage.

  2. i_bar1

    This code is meant to mimic yeast cells that produce Bar1, which degrades mating alpha factor and can cause negative feedback to reduce activity in nearby cells. The Kilobot will flash magenta every time it transmits a signal [value = 1], which may inhibit nearby bar1-sensitive Kilobots to reduce signal if exposed for long enough.

  3. i_sense_average_bar_rand

    This code allows Kilobots (yeast cells) to sense and transmit electronic signals (mating factor alpha) that will influence other Kilobots’ states and may influence its own state (self-sensing) in order to converge to a medium level of expression. The Kilobots are randomized into different initial states, representing different initial constitutive expressions. There are five possible states, which describe the Kilobot’s output: NONE [blue], LOW [cyan], MED [white], HIGH [orange], and MAX [red]. At each state, the cell lights up in the assigned color and transmits signals [value = 0] at the assigned frequency, NONE being the lowest level of frequency and MAX being highest. If a Kilobot senses enough signals transmitted by other Kilobots, it will go up in stage to the next highest level (so from NONE to LOW to MED). If the Kilobot initially is at a HIGH or MAX state, it will gradually reduce its expression until it matches that of other cells at the MED.

Results

ConvergeHigh

When all the Kilobots were programmed with code 1, they each started at different initial states, but they all reached maximum level of expression very quickly. Note that robots in the center seem to turn red faster, while the slowest robot to turn red is in the bottom corner, where it gets relatively fewer signals due to having fewer neighbors.

This progression from random to maximum is similar to how a noisy but highly expressing yeast strain would respond to mating factor alpha.

Diverging Bar1

In this clip, seven Kilobots were given program 1 and three were given program 2 (flashing magenta) to mimic Bar1 cells. This produced a diverging response, in which robots further away from the Bar1 Kilobots still reached maximum expression level, but those closer to the Bar1 Kilobots stayed at low levels of expression, eventually settling at a medium level of expression. This diverging response may be similar to what happens when different strains of yeast with and without bar1 are co-cultured.

RandomtoBimodal

In this clip, half of the Kilobots were programmed with code 1 and the other half were programmed with code 2. The initial state is similar to what happens in the first scenario (ConvergeHigh), in that initial response is random and noisy; however, the robots have clearly differentiated into medium and high levels of expression (white and red, respectively) by the end. This diverging yet coordinated response may be similar to what happens when two noisy strains of yeast are intermixed.

LowtoBimodal

In this clip, half of the Kilobots were programmed with code 1 and the other half were programmed with code 2, this time with a few modifications such that they all started at the lowest level of expression (blue for NONE). Again, the robots ultimately produced a bimodal response, with half of the robots emitting white light for medium expression and the other half emitting red light for maximum expression.

This diverging response may model what happens when two strains of yeast with the same initial response but different secondary responses are mixed together and induced with mating factor alpha.

  • 1. Blake, W. J., KAErn, M., Cantor, C. R., & Collins, J. J. (2003). Noise in eukaryotic gene expression. Nature, 422(6932), 633–7. doi:10.1038/nature01546
  • 2. Höfer, T., Krichevsky, O., & Altan-Bonnet, G. (2012). Competition for IL-2 between Regulatory and Effector T Cells to Chisel Immune Responses. Frontiers in Immunology, 3(September), 268. doi:10.3389/fimmu.2012.00268
  • 3. Gross, a, Rödel, G., & Ostermann, K. (2011). Application of the yeast pheromone system for controlled cell-cell communication and signal amplification. Letters in Applied Microbiology, 52(5), 521–6. doi:10.1111/j.1472-765X.2011.03035.x
  • 4. Gillespie, D. T., & Introduction, I. (2001). Approximate accelerated stochastic simulation of chemically reacting systems, 115(4), 1716–1733.
  • 5. Rubenstein, M., Ahler, C., & Nagpal, R. (2012). Kilobot: A low cost scalable robot system for collective behaviors. 2012 IEEE International Conference on Robotics and Automation, 3293–3298. doi:10.1109/ICRA.2012.6224638