Template:NYMU-2015project-wetlab

Experiment

Prevention

Concept

After P. infestans penetrates the cell wall of potato, it will exploit the potato and in turn infect potatoes nearby within 3 days. Since it will infect other potatoes in such a short time that there is no effective biological method to react against it and inhibit the development of the disease, we decide to prevent the disease at the very beginning by rendering the potatoes the ability to prevent the invasion of P. infestans. Under certain conditions, the zoospores of P. infestans will attach to the surface of potato leaves, penetrate the cell wall with high turgor pressure and some enzymes, and secrete effector proteins, such as Avr3, into potato cells. The binding of the effector protein to the transmembrane receptor PI3P is essential and helps mediate the translocation of the effector protein into the potato cell. It suppresses plant resistance-gene-based immunity; thus, P. infestans enter potato cells without any resistance. To stop the effector protein from entering potato cell, we found through literature research that reduction in translocated effector is a promising way to decrease the virulence of pathogens and improve disease resistance in potatoes. In previous research, FYVE protein domain from Hrs or EEA1 can also bind to PI3P receptor strongly in animal cells. We then decided to construct a FYVE protein domain with high affinity to PI3P that can compete with the effector protein to inhibit the entry of P. infestans.

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FYVE protein domain

The FYVE protein domain is a well-conserved PI3P-binding domain in various organisms with only 78 amino acids, originally existing in EEA1 and Hrs proteins of human and mouse, respectively, but also in many other proteins. However, according to literature research, EEA1 and Hrs protein are too large and may take a long time for the plant to degrade, so we extracted only the FYVE domain from Hrs protein, whose ability of binding to PI3P was better than others. Yet the natural monomeric FYVE has much lower affinity to PI3P than that of Hrs, and it is relatively unstable in the cytoplasm; we then constructed a dimeric FYVE to improve the conditions.

Experimental Design

We chose the dimeric FYVE as our competitive inhibitor against the Avr3 secreted by P. infestans. We first conducted modeling simulation, , but the dissociation constant of the monomeric FYVE domain is far higher than the RXLR domain of Avr3 and the former is quite unstable. After we connected the two FYVE domain by a linker protein to enhance the affinity and stability of the substrate, the dissociation constant was lowered from 250 to 3.8.

The Construction of dimeric FYVE

To construct the dimeric FYVE, we designed primers containing enzyme cutting sites---EcoRI, XbaI, SpeI, PstI--- and the coding sequence of the linker protein to extract monomeric FYVE domain from the Mus musculus embryonic cDNA. We then used restriction enzyme cloning to insert the dimeric FYVE into pSB1C3.

Circuit construction

pSAT6-EYFP-N1 is a vector which can express foreign genes in plant cells by transfection. We designed another set of primers to extract monomeric and dimeric FYVE from pSB1C3 and transfer it into the vector for transfection. The circuit is displayed on the right and contains viral constitutive promoters CaMV35S followed by the coding sequence of dimeric FYVE. The sequence coding for the enhanced yellow fluorescence protein follows that of the dimeric FYVE and is used as a reporter gene to indicate whether the dimeric FYVE works well in the plant.

  1. Dimeric FYVE

Transfection of plasmid DNA into plant cells

To check whether dimeric FYVE works well in plant, we inserted dimeric FYVE into vector PSAT6–EYFP-N1 and transfect the recombinant plasmid DNA into tobacco protoplasts, BY-2. BY-2 cells and potato cells share many common biological features as tobacco (Nicotiana tabacum) and potato (Solanum tuberosum) both belong to the Solanaceae family. If the dimeric FYVE works well in BY-2 cell, we can see yellow fluorescence on the endosome of the cell.

After transfecting the dimeric FYVE into BY-2 cells, we check whether the survival of the cell is influenced or not. Propidium iodide (or PI) is an intercalating agent and a fluorescent molecule used to stain cells and is the most commonly used dye to quantitatively assess DNA content. We stained the constructed BY-2 cell with PI for observation under the confocal microscope. The plant cell will be red if it is dead; on the other hand, if the dimeric FYVE does not influence the survival of the plant when binding to PI3P, the cell emits yellow fluorescence.

Second, we fused the cell with Wortmannin, a PI3K inhibitor restraining the synthesis of PI3P. If the binding between dimeric FYVE and PI3P is effective, inhibiting the production of PI3P will lead to the redistribution of dimeric FYVE into cytoplasm. To check if the dimeric FYVE actually binds to PI3P, we compare the engineered BY-2 cells containing dimeric FYVE, with only the experiment group treated with Wortmannin. The one treated by Wortmannin shows a dispersion of yellow fluorescence, and the other emitting concentrated light around the PI3P receptor. With this experiment, we confirm that dimeric FYVE not only expresses in the engineered BY-2 protoplast but also binds to the PI3P receptor as expected.

Promoter choice

Though the depletion of PI3P may be an effective way to prevent P. infestans from invading the potato, the constitutive expression of FYVE protein domain may cause certain physiological effects to the potato since PI3P is an important receptor for membrane trafficking to plants. Therefore, the timing of the expression of FYVE is the key to successful implementation of this strategy.

We chose promoter Gst1 as it will be activated in the potato cell within 24 hours of the infection of P. infestans and the production of it will be down-regulated after infection. Furthermore, promoter Gst1 will be mildly activated when the plant is wounded; namely, when the plant is most vulnerable to late blight.

Detection

Introduction

One major problem in controlling potato late blight is that there is no simple and convenient way to detect the disease. If we want to make sure that our potato has not been infected by P. infestans, we have to examine the potato in the laboratory for approximately a week. We want our detection system to be simple and convenient, and can instantly report the infection to the user. So the solution we reached is to design a soil based microbial fuel cell (SMFC) that can detect salicylic acid, a chemical that is produced when potato is injured or infected. With this device, we can know immediately at home whether the potato is infected.

We choose to utilize the Mtr (metal reduction) pathway of Shewanella oneidensis MR-1 to build our SMFC. Shewanella oneidensis MR-1 is a gram negative bacteria that is widely used for constructing microbial fuel cells because how it produce electricity is well characterized. The Mtr pathway contains 4 proteins: CymA, MtrA, MtrB, MtrC. CymA is a transmembrane protein that can transport electrons out of the cell membrane, and can then activate proteins mtrA, B, and C consecutively. From literature research, mtrB gene plays a pivotal role in stabilizing other component in this pathway. Therefore, in our project we utilize mtrB to create our biosensor by detecting changes in electric signals.

Design

To create this long term biosensor, we first knock the endogenous mtrB gene in Shewanella oneidensis MR-1. By reintroducing mtrB gene under the control of sensor (nahR), we can control the bacteria to generate electricity when it detects salicylic acid. We managed to design our sensor system using mtrB. However, the mere expression of mtrB is not enough. As electric signals in the soil can also be detected by the soil-based microbial fuel cell we created and can constitutively produce electric currents. This electric signal caused by soil itself somehow come as a background noise. When the electric signals emitted by the plant is not intense enough or when the number of bacteria in the soil drops, users may easily confuse it with the signals produced by the soil and make wrong judgments on pathogen control. Thus, we incorporate the oscillator into our circuit design. Even when the currents are not strong enough, users can still easily tell the difference between the electric signals emitted by the soil and those by the infected plant by recognizing the oscillating pattern of the latter. (See our modeling page)

Main circuit

As the circuit showed below, we first tested the oscillator and sensor with GFP as reporter. Then we replaced GFP with MtrB to test the expression of MtrB will also oscillate.

Results

Cure

Overview

Our goal is to kill P. infestans without harming the potatoes and environment. Fungicides used to kill P. infestans nowadays contain lots of heavy metal, such as Cu2+ that will do harm to both the plant and environment. At first we try to use some antimicrobial peptides; however this peptide will not only kill P. infestans but do harm to potatoes as well. Therefore, we try to look for the defensin that can weaken or even kill P. infestans from other plants. Although there are other chemicals that might be effective in inhibiting the growth of P. infestans, such as 2, 6-dichlorobenzonitrile, but some of these may also do harm to bacteria itself. Based on the above reasons, we finally choose lm-defensin, a defensing from maca that can effectively weaken and inhibit the growth of P. infestans

Circuit design

To purify defensin, we put a His tag on the N-terminal of the defensin. Also to produce lots of defensin, we choose T7 promoter and E.coli BL21 (DE3) which is used to express huge amount of proteins.

System

Our defense systen contains three different: prevention, detection, and cure. So how do we connect this three part together to form an impeccable defense system? First, we plant the genetically modified potato in the farm to prevent the disease from infecting the whole farm in a short period of time. Secondly, we implement the soil based microbial fuel cell that can detect and report whether the potato is infected or not immediately, in case that the potato can’t fend of the disease. When the SMFC detects the disease, it will send an electric signal that can trigger the spraying system. The spraying system will then spray the environmentally friendly defensin that we produced automatically. Man power is not required in the whole process except planting the potatoes.
Also, the defense system will also connect to a phone app that we created, so that we can know whether the potatoes are healthy or not. The defense system is localized, socialized, and mobilized.

Modeling

FYVE inhibition

This model was designed to investigate the competitive binding between FYVE protein domain and PI3P. Before we construct the circuit with FYVE, we have to determine whether the affinity between FYVE and PI3P is strong enough to compete with Avr3 effector protein secreted by P. infestans. We characterize this agonist-antagonist competition model by Gaddum-Schild equation. This model consists of two parts: monomeric FYVE and dimeric FYVE. According to paper research, monomeric FYVE is not so stable but the affinity of both monomeric and dimeric FYVE is quite high. The model is used to determine how much FYVE protein domain we should link with a linker so that the affinity of FYVE will be high enough to compete with Avr3.

Result

Conclusion

As we can see in the models dimeric FYVE is not only more stable but also have higher affinity to compete with avr3, the effector protein secreted by p.infestans. To create FYVE protein domain with higher affinity, we can link as much FYVE domain artificially as we want. However, it takes a lot of time to add a linker protein between FYVE and connect two FYVE together. Moreover, the longer the FYVE is, the longer it takes for the palnt to degrade the protein we created, which might have a negative impact to the potato. By constructing this model, we can find out the simplest way to create a competitive inhibitor and save the time for trial and error.

Parameters

Kd monomeric FYVE Dissociation constant of monomeric FYVE 420 nM Structural Basis for Endosomal Targeting by FYVE Domains
Kd dimeric FYVE Dissociation constant of dimeric FYVE 38 nM Phosphatidylinositol 3-Phosphate Induces the Membrane Penetration of the FYVE Domains of Vps27p and Hrs
Kd Avr3 Dissociation constant of Avr3 210 nM External Lipid PI3P Mediates Entry of Eukaryotic Pathogen Effectors into Plant and Animal Host Cells

Reference:

  1. Structural Basis for Endosomal Targeting by FYVE Domains
  2. Phosphatidylinositol 3-Phosphate Induces the Membrane Penetration of the FYVE Domains of Vps27p and Hrs
  3. External Lipid PI3P Mediates Entry of Eukaryotic Pathogen Effectors into Plant and Animal Host Cells

Oscillation

We construct a Lux/Aiia quorum-sensing oscillator so that S. oneidensis-MR1 will generate oscillating current which can help farmers tell if the potatoes are infected. We build two models to characterize the outcome of the genetic oscillator. The first model is used to predict whether the genetic oscillator will work. Since it is possible that some of the engineered S. oneidensis we inoculated on anode won’t survive, the second model is used to predict whether the gene expression will oscillate when there are only a few bacteria left on the anode.

Population simulation with delay differential equation

Since our circuit design is based on the circuit published by Danino et al.[1] our first model is a slight modification of the equation from the supplementary information. This model consists of four delay differential equation. We added one more delay differential equation to this model to simulate the expression of MtrB gene.

Parameters

Description Parameter Value
CA Synthesis constant of Aiia 1
CI Synthesis constant of LuxI 4
CmtrB Synthesis constant of MtrB 1(assume)
δ Hill function constant 10-3
α Hill function constant 2500
k1 Hill function constant 0.1
τ Time delay of the production of LuxR::AHL 10
k Kinetic constant of AHL synthesis 1
b Synthesis rate of AHL 0.06
γA Enzymatic degradation rate of Aiia 15
γI Enzymatic degradation rate of LuxI 24
γH Enzymatic degradation rate of AHL 0.01
γmtrB Enzymatic degradation rate of MtrB 0.007(assume,[3])
D AHL membrane diffusion 2.5
f 0.3
g Kinetics constant of AHL degradation 0.01
d0 Maxium cell density 0.88

Conclusion

As you can see in the graph, the production of Aiia and LuxI will oscillate as the time goes by. However, the production of MtrB will build up instead of oscillating. This is because MtrB is too stable and it is not totally degraded before the next period of oscillation. Thus, it is necessary to be targeted for proteolysis by adding a degradation tag in MtrB coding sequence.

Single cell simulation with ODE

If we inoculate the engineered S. oneidensis MR-1 into the anode of our SMFC, chances are that some of the bacteria will not survive in soil. Thus, it is necessary to simulate if the oscillator will work with only a single bacteria or a small population of bacteria.

With a small amount of bacteria, there will not have a significant time delay, and that’s why we chose a set of ODE to simulate the genetic oscillator.

Conclusion

As we can see in this model, even if there is only a small amount of bacteria left on the anode, the oscillator can still work. However, the current output will be much lesser than that of a large population of bacteria.

Parameters

Descriptions Parameter Units Value
Basal production rate LuxI AiiA a0LI a0A μM/min μM/min 7.79x10-6 6.18x10-6
Active production rate LuxI AiiA mtrB kpLI kpLA kpmtrB μM μM μM 0.9 0.84 0.33(assume[4])
Cell reaction rate AHL production rate LuxR AHL association rate LuxR AHL dissociation rate AHL::Aiia catalytic rate AHL conc. adjustsment for environment AHL membrane diffusion constant kp2 kr1+ kr1− kcataA ηenv ηcell 1/min μM/min 1/min 1/min 1/min 1/min 16 5.99x10-5 6x10-6 7x103 3x10-5 3
Michaelis-Menten constants LuxR::AHL complex Aiia complex KmLA KmaA μM μM 1.00x10-2 1.4977x103
Degradation related parameters LuxR::AHL complex AHL τLA τA/τA˜ 1/min 1/min 2.40x10-2 2.76x10-3
Enzymatic degradation Inverse of KMclx Vmax × ET OT / KMclx for LuxI Vmax × ET OT / KMclx for Aiia f δ1 δ2 δ3 1/μM 1/μM.min 1/μM.min 1/μM.min 4.12x10-2 7.16x10-1 2.50x10-2 0.99x10-2 (assume[4])

Reference

  1. Danino. T, Mondragon-Palomino, O, Tsimring, L. & Hasty, J. “A synchronized quorum of genetic clocks.” Nature 463, 326-330 (2010).
  2. Petros Mina, Mario di Bernardo, Nigel J. Savery, Krasimira Tsaneva-Atanasova. “Modelling emergence of oscillations in communicating bacteria: a structured approach from one to many cells.” Published 7 November 2012
  3. Danino. T, Mondragon-Palomino, O, Tsimring, L. & Hasty, J. “A synchronized quorum of genetic clocks.” Nature 463, 326-330 (2010).

Epidemic

P. infestans is an oligate parasite, which means it can’t complete its life cycle without exploiting potato. However, if a potato is infected by P. infestans, the disease will spread through the potato farm within 20 days.

In this model, we try to show the difference between planting the potato we engineered and planting potato that is susceptible or moderately resistant to potato late blight. Traditionally, SEIR model will be the perfect model to characterize epidemiology. However, we made some changes to the assumption of the traditional SEIR model so that the model can reflect the reality of potato late blight epidemiology much better.

Epidemology Symbol Meaning
Suspected The potato that is slightly or moderately resistant to P.infestans
Exposed Potatoes that are infected by a low amount of P.infestans, but they are still curable andwill not spread the disaese
Infected Potatoes that are infected by a large amount of P.infestans. Potatoes infected will spread the disease and jeopardize the whole potato farm
Immunized Potatoes that has been transformed by us and is resistant against P.infestans

According to our assumptions, we developed a set of ordinary differential equations that can characterize the epidemic of potato late blight.

Parameters

SEIR MODEL(COEFFICIENTS) γ transmission rate(1/days) 0.25
α Promoter activation rate(1/days) 1.0
ε Latent rate(1/days) 0.2 British Columbia Ministry of Agriculture[3]
μ Infection rate(1/days) 0.2
k Rate of resistance to late blight 0.75 External Lipid PI3P Mediates Entry of Eukaryotic Pathogen Effectors into Plant and Animal Host Cells[2]
beta Daeth rate of normal potato(1/days) 1/365
SEIR MODEL(VARIABLE) S The amount of all potatoes X(1)
E The amount of suspected individual X(2) Global analysis of an SEIR model with varying population size and vaccination
I The amount of infected individual X(3) [1]
R The amount of potato that survive X(4)
V The amount of potato being immunized X(5)

Result

As we can see from the model,with our genetically modified potatoes can prevent 80% of the potatoes from being infected, while only 20% of the moderately resistant potatoes can survive during the epidemic of potato late blight. Also as we can see in the graphs above, the survival rate do fit our sassumption the the potatoes under the destruction of late blight only end in two consequences----death(once the potatoes exposed to small amount of p.infestans are seriously infected ) or survive (once the “exposed potato” truned into “immunized potato”). This graphs not only show that our potatoes might be effective in fighting potato late blight in the real world but also show that potato late blight is a disease that needs to be take care of seriously and a impeccible system is necessary to fight against the disease.

Conclusion

As we can see from the model,with our genetically modified potatoes can prevent 80% of the potatoes from being infected, while only 20% of the moderately resistant potatoes can survive during the epidemic of potato late blight. Also as we can see in the graphs above, the survival rate do fit our sassumption the the potatoes under the destruction of late blight only end in two consequences----death(once the potatoes exposed to small amount of p.infestans are seriously infected ) or survive (once the “exposed potato” truned into “immunized potato”). This graphs not only show that our potatoes might be effective in fighting potato late blight in the real world but also show that potato late blight is a disease that needs to be take care of seriously and a impeccible system is necessary to fight against the disease.

Reference

  1. Chengjun Suna, Ying-Hen Hsiehc. “Global analysis of an SEIR model with varying population size and vaccination” doi:10.1016/j.apm.2009.12.005
  2. Kale SD1, Gu B, Capelluto DG, Dou D, Feldman E, Rumore A, Arredondo FD, Hanlon R, Fudal I, Rouxel T, Lawrence CB, Shan W, Tyler BM. “External Lipid PI3P Mediates Entry of Eukaryotic Pathogen Effector into Plant and Animal Host Cells” Cell. 2010 Sep 17;142(6):981-3.
  3. British Columbia Ministry of Agriculture