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In-situ Bioremediation of Antibiotics to combat Antimicrobial Resistance

Presented by Team IISER Tirupati 2020

Abhinaba Mazumder¹, Amogh Desai^{1, 4}, Jenochristina J P^{1, 4}, Omkar Mohopatra^{1, 4}, Purva Naik^{1, 4}, Purva S Damale^{1, 4}, Shubhrika Jain^{1, 4}, Srividya Vyjayanthi T^{1, 4}, R Rajalakshmi^{1, 4}, Tejas V Borker^{1, 4}, Uddeshya Pandey^{1, 4}, Ved Mahajan^{1, 4}, Yogeshwari A Kshirsagar^{1, 4}, Dr. Raju Mukherjee^{2, 4}, Prof. B J Rao^{2, 4}, Prof. G Ambika^{3, 4}

¹iGEM Team Member, ²Team Mentor, ³Team Faculty Advisor, ⁴Author, Indian Institute of Science Education and Research, Tirupati, AP, India

Aim of the Project:

The project aims to engineer a bacteria ‘Coli Kaze’ which is specifically able to degrade sulfonamides in animal excreta, and with reduced ability to undergo horizontal gene transfer, keeping biosafety in mind.

Abstract:

The irresponsible and off-label use of antibiotics in animal husbandry as growth promoters have contributed significantly to antimicrobial resistance (AMR). Phage therapy, the current best solution, is also failing due to CRISPR and other innate bacterial defences. To combat antibiotic pollution, we have developed a proof-of-concept model to engineer E. coli harnessing a two-component sulfonamide degrading system that degrades the sulfonamides present in poultry waste below the Predicted No Effect Concentrations, preventing selection for resistant strains. Our system renders the poultry waste antibiotic-free and makes it safe to use as manure. Surface exclusion genes have been integrated into the bacteria to reduce horizontal gene transfer. A user modulated ‘kill switch’ with a DNA degrading mechanism is also engineered to prevent the escape of AMR genes to the environment and ensure biosafety. This proof-of-concept model can be extended for other antibiotics by switching the antibiotic degrading genes involved.

Motivation

Anti-Microbial Resistance (AMR): A Global Concern

Fig 1: Deaths attributable to AMR every year compared to other major causes of death⁴.
Antimicrobials help treat and prevent serious infections. As bacteria that cause infection adapt to withstand antibiotics, the potential for antimicrobial resistance to cause a global health crisis looms large.

With the current of COVID-19 pandemic situation, there is a huge potential that certain activities could drive antimicrobial resistance even further¹. In addition, epigenetic modifications²and biofilm formation³ further increase bacterial resistance . Phage therapy which was thought to be the best solution is also failing due to CRISPR and other innate bacterial defenses⁴.

AMR in India:

Fig 2: Number of formulation companies manufacturing various antibiotics for animal use. Sulfonamides being one the highly produced antibiotics in India⁵.

AMR emergence in India is majorly driven by factors like the high burden of bacterial infections, poor sanitary, hygiene conditions, and the increasing proportion of intensive animal farming, and this is ramped by the unregulated access to antibiotics, including sale without prescription or with an invalid prescription.

AMR & Livestock Farms

Fig 3: More than 73% of all antimicrobials sold in the world are used in animals⁶
.Fig 3: More than 73% of all antimicrobials sold in the world are used in animals⁶.

The use of antibiotics as growth promoters in animal husbandry is a common practice and this, in turn, affects human health, as antibiotic-resistant bacteria can be transmitted between humans and animals through contact, food products, and from the environment.¹⁰

Antibiotics in Agricultural Waste

Fig 4: The graph depicts the detection of antibiotics in poultry: Poultry Litter, swine: Swine Manure, and beef cattle: Cattle Manure manure⁸.
Antibiotic class codes on the y-axis are as follows:
MC: macrolide, LM: lincosamide, TM: trimethoprim, TC: tetracycline,
SA: sulfonamide, PP: polypeptide, FQ: fluoroquinolone,
COC: coccidiostat, BL: Beta-lactam

From the data available from other nations it was noted that agricultural waste contains high levels of antibiotics. This is because of high consumption and incomplete metabolism in the animal bodies⁷.

References:

Saleem Z, Godman B, Hassali MA, Hashmi FK, Azhar F, Rehman IU. Point prevalence surveys of health-care-associated infections: a systematic review. Pathog Glob Health. 2019 06;113(4):191–205. DOI:http://dx.doi.org/10.1080/20477724.2019.1632070 PMID: 31215326
Ghosh, Dipannita, et al. "Antibiotic resistance and epigenetics: more to it than meets the eye." Antimicrobial agents and chemotherapy 64.2 (2020).
Patel, Robin. "Biofilms and antimicrobial resistance." Clinical Orthopaedics and Related Research^® 437 (2005): 41-47.
Oechslin, Frank. "Resistance development to bacteriophages occurring during bacteriophage therapy." Viruses 10.7 (2018): 351.
Fig. 1: https://www.euro.who.int/__data/assets/pdf_file/0005/348224/Fact-sheet-SDG-AMR-FINAL-07-09-2017.pdf?ua=1
Fig. 2: http://dbtindia.gov.in/sites/default/files/ScopingreportonAntimicrobialresistanceinIn dia.pdf
Fig. 3: Science 29 Sep 2017:Vol. 357, Issue 6358, pp. 1350-1352, DOI:10.1126/science.aao1495
Van Epps, A., Blaney, L. Antibiotic Residues in Animal Waste: Occurrence and Degradation in Conventional Agricultural Waste Management Practices. Curr Pollution Rep 2, 135–155 (2016). https://doi.org/10.1007/s40726-016-0037-1
Fig. 4: Van Epps, A., Blaney, L. Antibiotic Residues in Animal Waste: Occurrence and Degradation in Conventional Agricultural Waste Management Practices. Curr Pollution Rep 2, 135–155 (2016). https://doi.org/10.1007/s40726-016-0037-1
Landers, T. F., Cohen, B., Wittum, T. E., & Larson, E. L. (2012). A review of antibiotic use in food animals: perspective, policy, and potential. Public health reports (Washington, D.C.: 1974), 127(1), 4–22. https://doi.org/10.1177/003335491212700103

Introduction

Coli Kaze as the Solution to AMR

Our genetically engineered bacteria Coli Kaze would reduce the excessive efflux of antibiotics that are present in animal waste. Following degradation, our bacteria would undergo self DNA degradation and ultimately die.

Fig 1 : We would then be left with excreta free of both the antibiotics as well our engineered bacteria and could then be processed and used as safe manure. For proof of concept, we will initially target sulfonamides present in the poultry waste.

After the above process, cell lysate and enzymes could be found in the supernatant. Also to keep the temperature stable and suitable for enzyme action, the tank would be partially buried underground.

How much do we degrade?

No biological reaction is 100% complete and thus we cannot really say that we can totally eliminate the antibiotics in the environment. But we can degrade antibiotics to such an extent that natural selection would start to favour the strains that are not resistant in the environment. Such limits for various antibiotics have been predicted and are termed as the proposed no effect concentrations (PNEC)^{1, 2}. At these concentrations, the antibiotics in the environment would be so low that the gain of function mutation of AMR would be essentially rendered vestigial and would almost completely be eliminated by natural selection.

References:

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).
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.

Methodology and Parts

Coli Kaze is majorly divided into following three modules:

Module 1: Antibiotic Degradation

Fig 1: This Module involves degradation of sulfonamides in the animal excreta. For this purpose the genes sulX and sulR were integrated downstream to a medium strength expression system. The genes sulX and sulR are a two component flavin dependent monooxygenase system present in Microbacterium sp. CJ77.

Fig 2: Schematic depiction of two component flavin dependent monooxygenase system.

Module 2: Conjugation Reduction

Fig 3: Module-2 involves reducing conjugation thereby reducing horizontal gene transfer (HGT). To achieve this, the genes traS and traT are integrated downstream to a medium strength expression system in a F- chassis. The simultaneous expression of these genes in E. coli in a detergent free media has shown to reduce conjugation by 33,000 times. The genes traS and traT help in surface exclusion and are carried naturally by the E. coli F factor.

Module 3: DNA Degradation & Cell Death

Fig 4: Module 3 involves designing an user modulated ‘kill switch’ to induce cell death in the engineered bacteria. For this, we take use of the arabinose inducible promoter. The reason for choice of this promoter could be attributed to the high expression, tight regulation in presence of glucose and a cost-effective inducer. The toxin used downstream to this promoter is the bovine pancreatic DNaseI which is a well characterized endonuclease. The circuit incorporates the araC gene which is expressed in the opposite direction to that of the DNaseI. Both Plasmids will contain the kill switch to ensure biosafety.

Sulphonamide Detection

Graph is obtained by running the Poultry excreta sample dissolved in 10% methanol in the specified mobile phase.

In-Silico Modelling

Julia was used for running all simulation for models and computational studies.

Detection of Sulfadiazine in Poultry Excreta

Fig : The standard curve for Sulfadiazine from commercial tablets.
Absorption for the sample (λ_max)= 254 nm, Retention time = 13.099 minutes.

Fig : the HPLC curve for excreta dissolved in 10% Methanol.
Absorption for the sample (λ_max)= 254 nm.

Key Inference:

The peak at 13 minutes was collected for Mass Spectrometry.The peak in MS corresponds to the mass of Sulfadiazine. Thus, the presence of Sulfadiazine in excreta was confirmed.

Cloning the Kill Switch

Cloning Strategy

Restriction Digestion

Verification

Single Digestion: Linearization

Insert Release By Double Digestion

Antibiotic Degradation Model

Proposed Sulphonamide Degradation Mechanism:

Fig :Proposed sulfonamide degradation mechanism for the initial cleavage reaction mediated by sulX (sulphonamide monooxygenase) and sulR (flavin reductase).¹

Below 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.

Ordered bi-bi Mechanism:

Random Bisubstrate Mechanism:

The parameters 𝛂₁ and 𝛂₂ need to be determined experimentally for the medium in which the enzyme is working.

Ordered bi-bi Mechanism:

Random Order:

Key Inferences:

Decreasing the 𝛂₂ value increases the time taken to degrade antibiotics.
Increasing the 𝛂₁ value increases the time taken to degrade antibiotics.

Reference:

Kim DW, Thawng CN, Lee K, Wellington EMH, Cha CJ. A novel sulfonamide resistance mechanism by two-component flavin-dependent monooxygenase system in sulfonamide-degrading actinobacteria. Environ Int. 2019;127:206-215. doi:10.1016/j.envint.2019.03.046

Horizontal Gene Transfer Reduction Model

Transformation:

Key inferences:

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

Conjugation:

Key Inferences:

It takes about 36 hrs for the concentration of transconjugant mutant to become 1 cell/mL given our initial conditions.
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.
The concentration of transconjugants rises extremely slowly because of low growth rate, low mating pair formation rate and high mating pair breaking rate.

Transduction:

Key inferences:

The AMR mutant does not persist in the environment, but the cells harboring the virus particles with the AMR genes are stabilizing in the environment and so are the virus particles.

DNA degradation & Cell Death Model

Plot Ara C vs. Time:
To find excess arabinose molecules in system = (Number of AraC molecules needed for induction)x100

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

Plot c-AMP vs ext. Glu. vs Time:
To determine the amount of glucose needed, 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.

Inference: The maximum number of c-AMP molecules that can be present in a cell is 89727.8 molecules per cell.

Key Inferences:
Number of phosphodiester bonds concentration reaches 6143249 molecules at 1025.3 seconds = 17.09 minutes. This is the time needed for total genome degradation.

Conclusion:

Time required for degradation of DNA = 17.09 mins
Amount of arabinose needed = 0.0011179M
Amount of glucose needed = 0.056M = 1% w/v

Implementation

Fig1 :Overview of Proposed Implementation

ColiKaze is implemented in 4 phases and 2 systems:

Phase I : Pre Treatment
Phase II : Kill Switch Induction
Phase III : Incubation
Phase IV : Manure Processing

System I : Cell Free System
System II : Whole Cell System

Tentative Implementation Operation Schedule:

For 100 L slurry containing 15KG Animal Excreta
And ColiKaze Pre treatment culture volume 1L

Phase I : 60 min
Phase II : 10min
Phase III : 55 min
Phase IV : 100 min

Tank Model for System I

Tank Model for System II

Amount of L- arabinose added to media v/s Growth Medium(Kg)

Time of Incubation (hours) v/s Volume of Processed Amount of Excreta v/s Amount of Enzyme (uM)

Phase IV : Manure Drum Dry Processing¹

Reference:

Fig. adapted from Ymxfilter.com. 2020. Wedge Wire Trommel Screen, Wedge Wire Screen, Wire Screen Supplier.[Accessed 24 October 2020]

Outreach and Communications

Integrated Human Practices

Dr Abdul Ghafur
Coordinator of the Chennai Declaration on Antibiotic Resistance, MD MRCP FRCPath, Consultant in infectious diseases at Apollo Hospital, Chennai
Key Takeaways:
Antibiotic Resistance: the causes, the stats, and the possible solutions

Dr Sangram Bagh
Assistant Professor,
Saha Institute of Nuclear Physics
Key Takeaways :
Possible measures for AMR awareness and valuable inputs for the implementation aspect of the project

Dr Raghunath O R
Assistant Professor,
Department of Chemistry at IISER Tirupati
Key Takeaways:
1. Look for equations describing different kinetic equations as the mechanism of our enzyme is not known.
2. Introduction of two extra parameters to include the fact that our enzyme is a bisubstrate enzyme.

Dr V Bala Subramanian
Co-founder and Director at Bugworks
Key Takeaways :
Setbacks in the development of new antibiotics, importance of dry lab and essential suggestions for the project design

Dr Viswanadham Duppatla
AVP at IKP Knowledge Park
Ph.D. Research Scholar (Biochemistry), IISc. Bangalore
Key Takeaways :
Possible measures for AMR awareness and valuable inputs for the implementation aspect of the project

Science Communication

Interaction With High School Students at Agastya Foundation

Interaction With School Children at Nivdunge

Interaction With High School Students at Manav Rachna International School

Contributions and Achievements

Parts Contributions

We propose these parts for the very first time in iGEM and have documented the literature data for these parts in the parts registry (BBa_K3519000, BBa_K3519001).
We incorporate the genes traS and traT, (BBa_K3519002, BBa_K3519003) these parts have not been used in iGEM before and their data from literature have been added to their parts registry.

Achievements

Our mathematical models encompass all the aspects of our project and provide a proof of concept to validate our project.
Assured Bio-safety: As the engineered bacteria are killed before adding to the excreta, we would get a wider time window for degrading the antibiotics present in the excreta as there is no risk of the spread of antibiotic resistance genes.
Even though our project faced several difficulties this year, we were successfully able to complete the first phase of The Engineering Design Cycle involving Research, Imagine and Design with certain preliminary Build. Additionally, in a year-wide collaboration with iGEM IONIS Paris we could successfully develop clones with our 'kill switch' and are yet to characterize them.
As a team, we wrote three reviews one of which summarized our project in detail. Moreover, not just writing, we also got to peer review some of the other articles for the journal and appreciate the work of other teams in the competition.
Delivered a promising technological design and implementation that will be faster and efficient than all the technology currently used to tackle Antibiotic Pollution as bacteria grow and metabolize very quickly.

Acknowledgements and Sponsors

Acknowledgments:

We would like to thank the following people, labs and organizations for their assist and guidance throughout the project which helped us realize it to a vast extent.

We are grateful to Prof. K N Ganesh for constantly supporting us in each step of our journey.

We sincerely thank Dr Raghunath O R and Dr Tapan Chandra Adhyapak from IISER Tirupati and Matt Sinclair from Department of Biochemistry, the University of Illinois Urbana-Champaign for the aid and guidance with modelling the three modules of our project.

We heartily thank the iGEM-IONIS for carrying out some of the in-lab experiments which helped us characterize some of our parts successfully.

Sponsors:

@@ Line 654: / Line 654: @@
 <span style="font-family:roboto; font-size:18px; color:#93225d"><b>Proposed Sulphonamide Degradation Mechanism:</b></span><br><br><div class="borderpurple"><img src="https://static.igem.org/mediawiki/2020/d/da/T--IISER-Tirupati_India--Poster_sdm.png" align="top"></div><br><br>
 <span style="font-family:roboto; font-size:16px;"><span style="color:#93225d"><b>Fig :</b></span>Proposed sulfonamide degradation mechanism for the initial cleavage reaction mediated by <i>sulX </i>(sulphonamide monooxygenase) and <i>sulR </i>(flavin reductase).<sup>1</sup></span><br><br>
-<p style="font-family:roboto;font-size:16px;">Below 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.</p><br><span style="font-family:roboto; font-size:18px; color:#93225d"><b>Ordered bi-bi Mechanism:</b></span><br><div class="child2"><img src="https://static.igem.org/mediawiki/2020/c/c2/T--IISER-Tirupati_India--Poster_degradation10.png" align="top"></div><div class="clear"></div><br>
+<p style="font-family:roboto;font-size:16px;">Below 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.</p><br><span style="font-family:roboto; font-size:18px; color:#93225d"><b>Ordered bi-bi Mechanism:</b></span><br><br><div class="borderpurple"><img src="https://static.igem.org/mediawiki/2020/3/3e/T--IISER-Tirupati_India--Poster_letsjustkeeponaddingstuffonthelastdaydefinitelyagoodideajustonemore.png" align="top"></div><div class="clear"></div><br><div class="child2"><img src="https://static.igem.org/mediawiki/2020/c/c2/T--IISER-Tirupati_India--Poster_degradation10.png" align="top"></div><div class="clear"></div><br>
-<span style="font-family:roboto; font-size:18px; color:#93225d"><b>Random Bisubstrate Mechanism:</b></span><br>
+<span style="font-family:roboto; font-size:18px; color:#93225d"><b>Random Bisubstrate Mechanism:</b></span><br><br><div class="borderpurple"><img src="https://static.igem.org/mediawiki/2020/5/54/T--IISER-Tirupati_India--Poster_letsjustkeeponaddingstuffonthelastdaydefinitelyagoodideaanotherseriously.png" align="top"></div><div class="clear"></div><br>
 <div class="child2"><img src="https://static.igem.org/mediawiki/2020/3/34/T--IISER-Tirupati_India--Poster_degradation11.png" align="top"></div><div class="clear"></div>
 <br><br>
@@ Line 664: / Line 664: @@
 <p style="font-family:roboto;font-size:18px;"><span style="color:#93225d"><b>Ordered bi-bi Mechanism:</b></span></p><br>
 <div class="borderpurple"><img src="https://static.igem.org/mediawiki/2020/1/14/T--IISER-Tirupati_India--Poster_degradation2.png" align="top"></div><div class="clear"></div><br>
-<div class="borderpurple"><img src="https://static.igem.org/mediawiki/2020/3/3e/T--IISER-Tirupati_India--Poster_letsjustkeeponaddingstuffonthelastdaydefinitelyagoodideajustonemore.png" align="top"></div><div class="clear"></div><br>
@@ Line 670: / Line 670: @@
 <div class="borderpurple"><img src="https://static.igem.org/mediawiki/2020/f/f5/T--IISER-Tirupati_India--Poster_degradation3.png" align="top"></div>
 <div class="clear"></div><br>
-<div class="borderpurple"><img src="https://static.igem.org/mediawiki/2020/5/54/T--IISER-Tirupati_India--Poster_letsjustkeeponaddingstuffonthelastdaydefinitelyagoodideaanotherseriously.png" align="top"></div><div class="clear"></div><br>
 <p style="font-family:roboto;font-size:18px;"><span style="color:#93225d"><b>Key Inferences:</b></span></p><ul>
 <li><span style="font-family:roboto;font-size:16px;">Decreasing the <span style="color:#93225d">𝛂<sub>2</sub></span> value increases the time taken to degrade antibiotics.</span></li>
@@ Line 695: / Line 695: @@
 			<div class="title"> <span style="color:#93225d">Horizontal Gene Transfer Reduction Model</span> </div>
-			<!--Section text---><div class="text"><span style="font-family:roboto;font-size:18px;"><span style="color:#93225d"><b>Transformation:</b></span></span><br><div class="borderchild4"><img src="https://static.igem.org/mediawiki/2020/8/8f/T--IISER-Tirupati_India--Poster_hgt1.png" align="top"></div>
+			<!--Section text---><div class="text"><span style="font-family:roboto;font-size:18px;"><span style="color:#93225d"><b>Transformation:</b></span></span><br><br><div class="borderchild4"><img src="https://static.igem.org/mediawiki/2020/8/8f/T--IISER-Tirupati_India--Poster_hgt1.png" align="top"></div>
 <div class="clear"></div><br><span style="font-family:roboto;font-size:18px;"><span style="color:#93225d"><b>Key inferences:</b></span></span><ul>
 <li><span style="font-family:roboto;font-size:16px;">The double gene system buys us more time for degrading the antibiotics.</span></li>
@@ Line 708: / Line 708: @@
 <li><span style="font-family:roboto;font-size:16px;">The concentration of transconjugants rises extremely slowly because of low growth rate, low mating pair formation rate and high mating pair breaking rate.</span></li></ul>
 <br>
-<span style="font-family:roboto;font-size:18px;"><span style="color:#93225d"><b>Transduction:</b></span></span><br><div class="borderchild4"><img src="https://static.igem.org/mediawiki/2020/7/71/T--IISER-Tirupati_India--Poster_hgt3.png" align="top"></div><div class="clear"></div>
+<span style="font-family:roboto;font-size:18px;"><span style="color:#93225d"><b>Transduction:</b></span></span><br><br><div class="borderchild4"><img src="https://static.igem.org/mediawiki/2020/7/71/T--IISER-Tirupati_India--Poster_hgt3.png" align="top"></div><div class="clear"></div><br>
 <span style="font-family:roboto;font-size:18px;"><span style="color:#93225d"><b>Key inferences:</b></span></span><ul>
 <li><span style="font-family:roboto;font-size:16px;">The AMR mutant does not persist in the environment, but the cells harboring the virus particles with the AMR genes are stabilizing in the environment and so are the virus particles.</span></li></ul>