Team:IISER-Tirupati India/Results

Results

Detection of Sulfadiazine in Poultry excreta

Chromatogram of a standard solution of Sulfadiazine

Figure 1: This graph represents the standard curve for Sulfadiazine from commercial tablets in the specified mobile phase. Absorption for the sample was measured at λ_max = 254 nm. The retention time for Sulphadiazine peak was obtained at 13.099 minutes. The eluted peak corresponding to the retention time was collected and mass spectrometry was performed to confirm the peak corresponds to sulfadiazine.

Chromatogram of a standard solution of Ampicillin and its m/z plot



Figure 2: This graph represents the standard curve for ampicillin in the specified mobile phase. Absorption for the sample was measured at λ_max = 254 nm. The retention time for ampicillin peak was obtained at 3.819 minutes. The eluted peak corresponding to the retention time was collected and mass spectrometry was performed to confirm the peak corresponds to ampicillin.

Chromatogram of poultry excreta and the m/z plot



Figure 3: This graph is obtained by running the excreta sample dissolved in 10% methanol in the specified mobile phase. Absorption for the sample was measured at λ_max = 254 nm. A peak is obtained like the standard curve at the same retention time of 13 minutes. The collected eluted sample at this retention time was then taken for mass spectrometric analysis. It was observed that the peak in MS corresponded to the mass of Sulfadiazine. Thus, the presence of Sulfadiazine in excreta was confirmed.

Cloning

Cloning strategy



Figure 4: Figure depicts the cloning strategy of the different biobricks. bpDNaseI (BBa_K3519004), araC+araBAD (BBa_K3519010), mRFP (BBa_E1010) with double terminator (BBa_B0015), araC+araBAD promoter+bpDNaseI (BBa_K3519012), araC+araBAD promoter+mRFP (BBa_K3519011).

Amplification of gBlocks



Figure 5: PCR amplification of all biobricks and three vectors. The vectors used are pSB1A3, pSB4A5 and pSB4K5. All the biobricks were successfully amplified. The bpDNase showed non specific amplification.

Restriction digestion and assembly strategy



Figure 6: Restriction digestion and ligation strategy for the amplified biobricks. The digestion of the biobricks by 3A assembly followed by single pot ligation. The ligation products were directly transformed into competent DH5-alpha bacteria.

Restriction digestion and assembly strategy



Figure 7: Transformation plates for DH5-alpha. There were significant number of colonies observed for the DNASEI and mRFP plates. Few colonies were also obtained on the negative control plates. The colonies from all plates were screened using colony PCR for positive clones.

Colony PCR



Figure 8: Colony PCR of clones from the transformation plates. The DNASEI-pSB1A3 clones showed amplification at expected band lengths for Colony 1 and Colony 4. These colonies alongside other colonies were patched onto another plate and were cultured.

Patched colony PCR plates



Figure 9: Patched plates from before Colony PCR. All screened colonies showed good growth on LB Agar plates with antibiotics. All these colonies were cultured and their plasmid were isolated. The plasmids were further used for insert release and linearisation experiments.

Linearisation



Figure 10: Linearisation of isolated plasmids of colonies from the patched plates. The white arrows represent the suspected positive clones.

Insert release by double digestion



Figure 11: Insert release by double digestion of the isolated plasmids. The white arrow shows the ones that gave a positive ID for clones. A total of four clones showed positive results.

Conclusion

The pSB4K5-DNASEI clone 1 and pSB4K5-mRFP clone 1 gave positive results by both linearisation and insert release experiments. Though, these clones are thought to be positive, they are yet to be confirmed by sequencing, which could not be done due to time limitations. Further experiments with these parts have been designed and are yet to be conducted.

Modeling

Module 1

For this simulation, we have assumed antibiotic concentration of 50 uM and enzyme of 0.75 uM (SulX). These values can be easily changed as per the user’s requirements. We have assumed these values as these were the values that have been experimentally verified in the paper. We have taken the antibiotic in consideration is sulphamethazine. Degradation products for sulfamethazine are 4-aminophenol, sulphur dioxide and 2-amino-4,6 dimethylpyrimidine.

The Michaelis menten constants for sulfamethazine are:

                           

Table 1
Symbol	Description	Value [1]
KD	Concentration of Drug at which the rate is half of Vm	16.83 uM
KCat	Rate constant for rate limiting step	0.58 * 46,000 = 26 sec-1

References:

1. [1]:Kim, Dae-Wi, et al. "A novel sulfonamide resistance mechanism by two-component flavin-dependent monooxygenase system in sulfonamide-degrading actinobacteria." Environment international 127 (2019): 206-215

Ordered bi bi mechanism

Considering the initial conditions given above, we solved the differential equation and plotted concentration of enzyme vs time graph for various values of 𝛼₁ and assuming 𝛼₂ to be equal to one. As 𝛼₁ increases, the effective K_D increases and this results in slower enzyme action. This can be seen from the plot below.



The green line is the line marking the time when the concentration of antibiotics fall below PNEC level i.e., about 99.95% degradation in our case[2][3].

In order to see how the values of 𝛼₁ and 𝛼₂ change the time taken for degradation, we plotted a heatmap.



As per the equation, time taken to degrade the antibiotics increases when 𝛼₁ is increased and 𝛼₂ decreases. This can be seen from the heatmap as on the top right area where 𝛼₁ is the highest and 𝛼₂ is the lowest, time is maximum.

References:

1. [2] Greenfield, Ben K., et al. "Modeling the emergence of antibiotic resistance in the environment: an analytical solution for the minimum selection concentration." Antimicrobial agents and chemotherapy 62.3 (2018).
2. [3] Bengtsson-Palme, Johan, and DG Joakim Larsson. "Concentrations of antibiotics predicted to select for resistant bacteria: proposed limits for environmental regulation." Environment International 86 (2016): 140-149.

Random order

For random order, we did the same analysis for random order and found the degradation curve and the heatmap.



The curves in random order are slightly higher than the ordered bi bi case as the denominator of random bisubstrate is higher than the ordered.
This slight increase in the time is also evident in the heatmap.



However, the increase in time is not very significant as we can see from the histogram.

Key Takeaways:

1. 1. As per the formulation of the model, these set of equations can be used in any situation as it takes care of the fact that our enzyme is working in a non ideal medium. The parameters 𝛂₁ and 𝛂₂ need to be determined experimentally for the medium in which the enzyme is working.
2. 2. Decreasing the 𝛂₂ value increases the time taken to degrade antibiotics.
3. 3. Increasing the 𝛂₁ value increases the time taken to degrade antibiotics.

Module 2

Here we aim to see how the bacterial population evolves when the engineered cells containing antibiotic resistance gene and find the time it takes for the transformation mutant to reach the threshold value of 1 cell/mL (chosen).

Transformation

Transformation is the process in which the cells pick up plasmid from the environment.

For this simulation, we have taken the initial concentration of engineered cells to be 10⁷ cells/mL and wild population to be 10⁵ cells/mL. The initial concentration of transformed mutants and plasmid in the environment is taken to be 0. Following are the plots which we got by solving the transformation model for single and double gene system.

Engineered cells

The engineered cells i.e., Coli Kaze keep increasing in both cases increase during the entire simulation. This increase is due to the growth rate of the bacteria.

Wild population



The empty cells increases initially and then starts to decrease as it is converted into transformed mutants. The decrease in double gene is more than the single gene as there are more plasmids in the environment because the engineered cells upon death give two plasmids instead of one as in single gene.

Transformed mutants

The transformation mutant in single gene is the cell that was empty earlier and picked up the plasmid where as in the double gene system the transformed mutant is the cell which has taken up both the plasmid from the environment. We expect that in the double gene case, the mutants will take longer to grow as the cell has to pick up two plasmids. This is very well evident from the plot below.



The green line and the purple line marks the time it takes for transformed mutants to become 1 cell/mL in a single gene and the double gene system respectively. Purple line (3.4hrs) being on the left of the green line (0.20 hrs) tells us that the double gene system does make the formation of transformed mutants slower. The 1 cell/ml threshold can easily be changed as per the users requirement. This module shows us the importance of using the double gene system in the implementation of the whole cell system. The increased biosafety of our project using the double gene system is validated through this model as it clearly shows an increased time for the formation of AMR mutants.

Key Takeaways:

1. 1. The double gene system buys us more time for degrading the antibiotics.
2. The intrinsic death rate due to the environmental conditions plays a huge factor in the formation of AMR mutants due to transformation.

Conjugation

Conjugation is the process in which the bacteria transfers genetic material by direct contact. The donor (one having the F plasmid) forms a mating pair with the recipient. The transfer of genetic material happens via a pilus, that is formed during formation of the mating pair.

For our simulation we have used the number of recipients (R) = 10⁷ cells/mL, donor (D) = 10⁵ cells/mL and the number of transconjugants was taken to be 0 as there are no transconjugant mutants to begin with.

The following are the plots that we generated by solving the Differential equation model of conjugation.

Donor



The donor cells increase in the timespan of our simulation due to the growth rate being the dominant term in the equation

Recipients



The recipients increase exponentially in the first 50 hours because of the dominating growth rate. When a donor and recipient form a mating pair the mating pair cannot grow. We see that the number of transconjugants formed is significantly lesser than both the donors and the recipients. Since the initial conditions involve a much higher number of recipients, there isn't much conversion into transconjugants at all. Thus the recipients dominate in the given time span of the simulation. Since we wouldn’t be using our system for more than 50 hrs we simulate our model for only this period of time.

Transconjugant



We wanted to keep track of when the first transconjugant is formed per ml of culture. We consider this as a threshold before which all our enzymatic degradation must be complete. We see that without the use of our surface exclusion genes we have about 36 hrs (the orange vertical line) for enzymatic degradation of antibiotics. Since we are using the surface exclusion genes in our system we see that our threshold is not even close to being reached. This simulation gives us important validation of our surface exclusion genes and their effect on the biosafety of our project. This threshold of 1 cell/ml of culture that we have chosen is arbitrary and can be changed as per the user’s requirement.

Key Takeaways:

1. 1. It takes about 36 hrs for the concentration of transconjugant mutant to become 1 cell/mL given our initial conditions.
2. 2. The two proteins TraS and TraT reduce the formation of transconjugants to such an extent that with these initial conditions, it takes about 50 hrs to form one mutant in a 100L slurry.
3. 3. The concentration of transconjugants rises extremely slowly because of low growth rate, low mating pair formation rate and high mating pair breaking rate.

Transduction

Transduction is the process of DNA transfer in which the bacterial genome is take up by an attacking virus particle and is then transferred to some other cell by that virus.

For our simulations, We have taken initial conditions as 10⁷ Coli Kaze (Nsus2), 10⁵ Wild susceptible population (Nsus1). All the other bacterial populations were considered to be 0. The number of circulating virions were taken to be 10⁷. Population cap was supposed to be 10¹⁰. Let us now look into the dynamics of each population one by one.
The following are the plots of all the species involved in the transduction process.

Susceptible Population

The number of susceptible bacteria initially increase as the growth rate is the dominating term in the equations. After a few hours The number of phages in the population increase as they infect and replicate in the hosts. This leads to a decrease in the number of susceptible bacteria as they are gradually converted into other forms in the bacterial population. The intrinsic death rate in the environment also contributes to this decrease in their number. The susceptible population of bacteria will be gradually wiped out in the population as the growth rate is the only term that contributes to its increase. Another term that leads to their increase in number is the number of lysogenic bacteria that do not have any immunity to phages. But these also quickly decrease in number as they can be infected by other phages and can induce lysis through direct or induced lytic cycles.



Newly Infected Population

The newly infected population comprises of bacteria that were infected by phages. The population of bacteria shows a steep increase in the beginning but at a time point in the graph, there is a small dip in the rate of increase after which it again starts increasing. This small dip is most likely due to the increase in the number of immune lysogens in the population as they are exponentially increasing. The newly infected population gradually increases until it hits a peak and then gradually decreases. The decrease is due to the continuous loss of the susceptible population of bacteria and continuous increase of immune lysogens in the population that cannot be infected by viruses. The newly infected population gradually decays in the population.



Bacteria processing the transduced particle

This population comprises of bacteria that are infected a transduced particle (accidentally packaged non-viral DNA). These transduced particles could either contain the AMR gene or not. If infected with a transduced particle the bacteria would again switch to the susceptible state in the population after processing the transduced particle. We use the fraction of Coli Kaze in the bacterial population to predict the fraction of AMR bacteria in the population. The viruses that infect Coli Kaze have a probability of picking up the AMR gene and transmitting it to the wild population. The wild population that gets infected with the AMR gene is what we track when we count AMR mutants. We do this for both single and double gene systems.



Immune Lysogenic Population

This population of bacteria undergoes exponential growth. We have run the simulation for 150 hrs and this trend continues. In previous simulations we have noticed that this population is the final population that saturates at the population cap. Immune lysogens cannot be infected by viruses and thus persist in the environment as death rate is the only term that reduces their number apart from induction of lytic cycle. Therefore they constantly grow in the population and lead to an increase in the number of phages and constantly replenish the phage population.
Upon running the simulation for longer times we notice that the viral population also stabilizes. This is because the immune lysogenic population has stabilised and this was the only population that could increase the number of viruses as at later time points there is no more susceptible population left to infect.



Induced Lytic Population

This population consists of the lysogenic bacteria that have had their lytic cycles induced by the virus. This population continuously increases as the number of lysogenic bacteria keep increasing in the population and also finally stabilize with the immune lysogenic population.



Direct Lytic Population

This population consists of bacteria that are infected with a virus and are immediately lysed by the virus. Since there are no lysogenic bacteria in this population, they also gradually decay with time after reaching a peak.



Circulating virions

The virions population consists of viral particles and transduced particles. The viral particles can replicate in the host upon infection. During mistakes in packaging its own DNA it could lead to the formation of transduced particles that could contain AMR gene. These transduced particles can infect the wild population to form AMR mutants. The virions continuously grow and replicate in their hosts that are abundant in the population. Therefore they follow exponential growth during the course of the simulation.



AMR Mutants

As we see the AMR mutant population in the bacteria for both single and double gene systems does not increase by much and finally decays. While this would seem to be a good thing, The phage population immune lysogenic population also stabilizes with time. The phages that infect our bacteria would lead to a constant production of transduced particles that contained the AMR gene. Even upon induction of killswitch, these packaged AMR transduced particles would still persist in the population. Upon release into the environment, these transduced particles could contribute to the spread of AMR. Therefore further treatment would be necessary against these viral particles before release into the environment while using the whole-cell system. The whole-cell system would need further development for practical use and would be a future aspect of the project. This model validates the essence of the double gene system showing us it is useful in reducing transduced mutants in the population.



Key Takeaways:
The AMR mutant does not persists in the environment, but the cells harbouring the virus particles with the AMR genes are stabilizing in the environment and so are the virus particles.

Inferences:

1. 1. Out of all the three HGT mechanisms, transformation happens the fastest and this gives us about 12 min in the single gene system and 3.4 hrs in the double gene system for the given initial conditions.
2. 2. The transduction model suggests that there are virus particles having packaged the AMR gene that persist in the environment .These particles can propagate the AMR gene after being released in the environment. Hence, we need some method to destroy these virus particles before release into the environment.
3. 3. Our HGT models suggest that transformation is the biggest problem as the rate of formation of AMR mutants is very high. Addition of reagents to reduce this process could be a possible solution to implement the whole cell system.

Module 3

From module 1, we found that the proteins produced for antibiotic degradation work effectively. We also saw that using a double gene system and other proteins produced in module 2 reduce horizontal gene transfer of genes with other bacteria.

In module 3, we find out the following:
1. Time required for DNA degradation.
2. Amount of arabinose molecules needed for effective induction of the ‘kill-switch’.
3. Amount of glucose needed for efficient function of the ‘kill-switch.

We assume that before the induction of DNA degradation, the bacteria is incubated for some time so that the proteins required for antibiotic degradation are produced.

Variable Table
Variable	Full form	Explanation	Initial Value
G1	araC genes	Number of araC genes in a cell	225
R1	araC RNA	Number of araC RNA's in a cell	0
P1	araC protein	Number of araC molecules in a cell	10750 (annexure 2 -8*)
A	Arabinose sugar	Number of molecules of arabinose sugar outside the cell	-
𝛘1	Arabinose sugar bound to araC	Number of A-araC complexes in a cell	0
θ1	araC protein bound to P-bad promoter	Number of actively repressed p-bad promoters in a cell	225
θ2	A-araC (arabinose sugar araC complex) bound to I1 and I2 sites of DNA) near p-bad promoter (this actually releases the DNA loop to give free promoter)	Number of active P-bad promoters n a cell	0
θ3	A-araC-CC (arabinose sugar araC complex bound to I1 and I2 sites and CAP-cAMP complex bound to the CAP binding site of DNA) near to P-bad promoter (this enhances the efficiency with which RNA polymerase binds to the promoter )	Number of promoters which have enhanced activity in a cell	0
𝛘2	c-AMP molecules	Number of c-AMP molecules in a cell	89727.8 (annexure 2- 9*)
Eg	External glucose	Number of moles of glucose sugar outside the cell	-
𝛘3	Catabolite activator protein (CAP)	Number of CAP molecules inside a cell in free form	1594.5 [1]
𝛘4	CAP bound to c-AMP	Number of CAP-c-AMP complex in a cell	0
G2	DNASE1 gene	Number of DNASE1 gene in a cell	225
R2	DNASE1 mRNA	Number of DNASE1 mRNA in a cell	0
P2	DNASE1 protein	Number of DNASE1 molecules in a cell	0
S	Substrate concentration	Number of phosphodiester bonds in the DNA of a cell	12286498 (annexure 2- 11*)
vc	Rate of c-AMP production (depending on external glucose concentration)	Number of c-AMP molecules produced in cell per second	0
GT	External genes added to cell	Total number of gene segments added to cell (both araC and DNASE1)	225*G1*G2I(annexure 2- 10*)

Plots

To determine the number of arabinose molecules needed for the effective induction of the ‘kill-switch’, we find the number of AraC molecules needed for induction and multiply it by 100 (as those many numbers of arabinose molecule are assumed to be excess for the system)

Inference: the maximum value of AraC protein in the system is 10570 molecules.


Inference: This graph verifies that arabinose-AraC complex formation follows Hill’s equation.

Assuming that a 100 times more arabinose molecules would be needed, 1057000 molecules of arabinose sugar molecules per cell i.e 0.0011179M solution will be needed for effective induction of the ‘kill-switch’.

Now, to find the amount of glucose needed, we plot a graph of External glucose concentration vs c-AMP molecules per cell vs time. We observe that at higher time points, the graph of External glucose concentration versus c-AMP remains almost the same. So, plotting a graph of External glucose versus c-AMP molecules per cell at 3600 seconds, we aim to find the External concentration of glucose for the maximum amount of c-AMP molecules that can possibly be present in a cell.

The maximum number of c-AMP molecules that can be present in a cell is 89727.8 molecules per cell (annexure 2-9*). At any minimal concentration of glucose, this value is easily attained. So, for our convenience, we take the external glucose concentration to be 0.056 M.



Inference: for any minimum glucose concentration, the number of c-AMP molecules per cell exceeds the maximum value or the upper limit of 89727.8 molecules per cell (annexure 2-9*). So, any minimal concentration of glucose would work for the efficient functioning of the ‘kill-switch’. For convenience, let us take that as 0.056M.



Inference: Substrate concentration reaches half of the maximum (attains entire genome degradation) at time 17.5 minutes.

But by the time this happens, the genes coding for AraC and DNaseI protein also would have degraded. As the time taken for degradation is very less, the degradation of the genes would hardly matter. But to be accurate and develop a rigorous mathematical model, we follow annexure 2-12* to obtain the time only after which the araC and DNASE1 genes start to degrade as 1025s. (this result is same as assuming that the genes of module 3 are the last to degrade). At 1025 seconds, S=6148423 molecules, R₂=294.271, P₂= 91.93. Using these as initial conditions, we reconsider the system of equations (12) and (13) together and plot S vs time graph from the time 1025s.


Inference: Substrate concentration reaches 6143249 molecules at 1025.3 seconds = 17.09 minutes. This is the time needed for total genome degradation.

Conclusions

In order to find out how time depends on the amount of antibiotics and enzymes, we plotted a 3D graph with amount of excreta and amount of enzyme as the independent axis and time as the dependent. This would be specific for the whole cell system.
The implementation of the whole cell system would be similar to the cell free system except the fact that we would be using cells that would take in the antibiotics and degrade them. For our purposes we have considered the uptake rate to be instantaneous and the system would function exactly as the cell free system but the degradation would be time bound by HGT. (Please refer to Proposed Implementation for more details)

We use the ordered bi bi equation to plot the following graph to visualize the interdependence of the three quantities.

For this plot we use,
Final Concentration of antibiotics as 99.95% degradation of initial amount.
𝛼₁ = 0.7 and 𝛼₂ = 0.8
K_D and K_cat values are as per Table 1.

After substituting these values in



We get the final equation as,



We took the bounds for excreta and enzyme to be from 1 kg to 100 kg and 0.01 uM to 1 uM and plotted the following graph



The whole cell system would never be devoid of HGT and it would be better to use a cell free system as in theory it totally prevents HGT. However in cases where the cellular physiology of the GMO is necessary for degradation of the antibiotic we would require the whole cell system as a backup for the degradation of antibiotics.

In order to summarise our project idea, the following flowchart shows the major advantages and disadvantages of our project ideas and gives us an idea of how all they all connect and orchestrate.



As the pandemic had closed the lab doors, There was a special emphasis on mathematical modelling to validate our project. We have 2 methods of implementing our project, The cell-free system and the whole-cell system. Our models capture both these implementations. Our mathematical models consist of 3 main modules. The aim of the first module was to find out the time required for antibiotic degradation. We have used bisubstrate enzyme kinetic equations to model this. The speciality of this module lies in the alpha values. These are sort of efficiency factors of the enzyme that would change from system to system and must be experimentally determined before using the models. These alpha values make our module very robust and easily applicable to any scenario. The second module aimed to identify the time required for AMR mutants to form through HGT. We had to implement the killswitch before the formation of any HGT mutants and also degrade the antibiotics. Thus this time was very essential To ensure that our enzymes are fast enough to degrade the antibiotics without the formation of any antibiotic resistant mutants. We also try to predict the reduction in HGT due to the conjugation prevention genes and the double gene system thereby confirming the efficiency of our circuits in the overall biosafety of the project. This module mainly expresses the difficulties of using a GMO for this purpose and suggests the use of a cell free system is the most ideal for the environment. The final module was focused on the time taken for the complete digestion of DNA by our killswitch so that there is no available DNA containing the AMR genes for uptake through transformation when it is released into the environment. This part of our project tells us the minimum time required to incubate the killswitch with the inducer. It also takes into account catabolite repression which was used to keep our killswitch from being induced when no inducer is present. Our mathematical models encompass all the aspects of our project and provide a proof of concept to validate our project.