iGEM 2025 — Team iisc-bengaluru

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# Lab Notebook

A chronological record of our team's experiments, results, and discoveries throughout the iGEM season.

April 2025

Week 1 & 2: 17 April - 30 April

* We familiarised ourselves with the SnapGene software since we would use it to optimize and understand the translation of these genes.
* We studied the components of the plasmids we planned to use and considered the restriction sites we could use, choices of operons, and inducer promoter options.
* We also planned soil experiments to test soil samples for nitrate, nitrite, and ammonium levels in a lab.

May 2025

Week 3 & 4: 1 May - 14 May

* We finalized the restriction sites to be used on the pSEVA248 with input from dry lab data, and planned the workflow for Wetlab.
* We received a stab of pSEVA248 in *E. coli* from the SEVA Repository; however, streaking did not result in any growth.
* We worked on ordering the pSEVA248 plasmid from the SEVA Repository using a different channel to ensure that it could be cultured.
* Started modifying our chosen operons to fit the restriction sites on the pSEVA248, using SnapGene.

Week 4 & 5: 15 May - 28 May

* We underwent thorough training on basic laboratory safety, as well as common techniques and the use of lab equipment.
* Prepared stocks of broths, agar plates, and antibiotics.
* We decided the *E. coli* strains we would use as a chassis, along with *P. putida*, based on availability in our PI's lab.
* We contacted Phale Lab in IITB to obtain the *P. putida* KT2440 strain.

Week 5 & 6: 29 May - 11 June

* We received *P. putida* EM42 along with pSEVA 248 in *E. coli* from the SEVA Repository. Despite repeated attempts to culture the *E. coli*, we were unsuccessful.
* We also received the *P. putida* KT2440 from Phale lab as an agar stab. Both *P. putida* KT2440 and *P. putida* EM42 stabs were placed in cold storage until we received approval for our check-in form.

Week 7 & 8: 12 June - 25 June

* Based on a preliminary comment on the safety form, we used the BSL-2 Facility in our lab to plate both strains.
* Both cultures grew successfully, and primary and secondary glycerol stocks for the same were prepared.
* Since there was an issue with our agar, the plates did not solidify properly, and we had to remake them.
* Worked on Codon Optimisation for nitrite reductase genes to be ordered from TWIST Bioscience. Placed order for *nrfHAIJ, nrfABCDEFG* and *nirBD* operons. The former two were split into two parts to fit the 5kb synthesis limit.
* Since we had failed to culture pSEVA248 from stabs, we considered getting the plasmid synthesized, as the inserts were tailored to the MCS of the SEVA backbone.
* We found two colonies on the plate we made for the pSEVA248. We suspected it to be a contamination due to colony morphology and colour. We performed a Gram stain with extra precautions for a preliminary test. The colonies were in tetrads and were Gram-negative, indicating it was a contamination.

June 2025

Week 9 & 10: 26 June - 9 July

* Our clonal genes were delayed at customs, but arrived in the second week of June. However, the second part of the *nrfHAIJ* operon could not be synthesised due to its complexity.
* We received *nirBD, nrfABCD, nrfEFG,* and *nrfHA* as dried clonal DNA.
* We still did not have a plasmid and were considering options on Addgene, since plasmid synthesis would be too time-consuming and a drain on limited financial resources.
* Meanwhile, the SEVA team attempted to send the pSEVA248 plasmid to Phale Lab at IIT Bombay, where they would culture it and share it with us.

Week 11 & 12: 10 July - 23 July

* While SEVA shipped the plasmids, we attempted to save any remaining plasmid from the agar stabs based on a suggestion from our PI. We eluted the agar and added lysis reagents, followed by a plasmid isolation protocol.
* We attempted to transform this into *E. coli*. This failed, and we had to wait until Phale Lab sent us the plasmid.
* We resuspended the dried clonal genes and transformed them into competent *E. coli* DH5-alpha successfully, for the *nrfABCD, nrfEFG* and *nirBD* operons. We did not transform *nrfHA* since the second part of the operon had not been synthesized.
* Phale lab could not culture pSEVA248. However, they sent us other SEVA variants with different ORIs and resistances in *E. coli*. We received pSEVA238, pSEVA258, pSEVA268 and pSEVA2313.
* We visited Berambadi Village and collected soil and water samples.

Week 13: 24 July - 30 July

* Our wet lab team was travelling, hence no lab work was possible.
* After travelling to Mumbai for the All India iGEM Meet, we travelled to Pune and collected soil and water samples from Talegaon Village.
* Samples were collected in Eppendorf while wearing masks and gloves.

Week 13: 31 July - 6 August

* We replated the *P. putida* strains, the *E. coli* containing clonal genes, and the *E. coli* containing plasmids.
* Prepared glycerol stocks for all these.
* Inoculated the plasmid containing *E. coli* in LB + Ampicillin overnight.
* Isolated plasmid samples using the QIA Spin Miniprep Kit and quantified with a nanodrop.
* The quality of the isolate and yield was low, so we did not move ahead with digestion.

August 2025

Week 14: 7 August - 14 August

* Performed the second set of inoculations. Attempted to grow culture in 50 mL Falcon tubes to increase yield.
* Attempted manual digestion instead of the kit at the suggestion of our mentor, since it would give a better yield.
* Digested pSEVA648 with EcoRI and BamHI. While isolation worked, the digestion did not.

Week 15: 15 August - 21 August

* Since we faced issues with dual digestion, we tried to digest with only one restriction enzyme at a time. EcoRI did not work, BamHI was inconclusive, and HindIII seemed to have worked.
* Replated the cultures since 2 weeks had passed.
* Image: Marker, Undigested pSEVA648, pSEVA648+EcoRI, pSEVA648 + BamHI, pSEVA648+ HindIII
![Image: Marker, Undigested pSEVA648, pSEVA648+EcoRI, pSEVA648 + BamHI, pSEVA648+ HindIII](http://static.igem.wiki/teams/6006/wiki/undigested-ladder.webp)

Week 16: 22 August - 28 August

* We tried another set of inoculations and digestions, this time with the pSEVA258 plasmid.
* The digestion had some impurities, but indicated that the MCS on the plasmid did not have an issue.
* The extended band was likely due to a longer incubation time, and we decided to reduce it in the next iteration.

Week 17: 29 August - 4 September

* Designed different media proven to stimulate DNRA and prepared them to check for optimum *P. putida* growth.
* One more set of isolations and digestions. Elution was unsuccessful.

September 2025

Week 18: 5 September - 11 September

* Got a good yield from isolation and gel, but elution failed and did not show any DNA.
* Performed one more set of isolations and digestions. Elution was successful this time.
* Attempted ligation - failed.

Week 19: 12 September - 18 September

* Received the nitrite testing kit we had purchased.
* Prepared a set of inoculants that did not give a good yield.
* Obtained T4 DNA ligase and buffer, and moved testing to a lab closer to our classes so we could work longer hours.

Week 20: Mid-September

* Due to mid-semester exams, we were not able to work in the lab during this time.

Week 21: Late September

* Tested for basal expression levels of nitrite reductase genes for *P. putida* KT2440 and *P. putida* EM42 in nitrate and nitrite medium.
* Made a set of isolates, but they had low yield and were highly contaminated.
* A second set of inoculants showed moderate yield, but elution failed and showed erroneous values on the nanodrop.
* Began testing soil samples in the Bagchi Lab for nitrate, nitrite, and ammonium levels.
* Performed basal biofilm formation assay for *P. putida* KT2440 and *P. putida* EM42.

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# Wet Lab

## Cycle 1

Design

Build

Test

Learn

Design

Establishment of metabolic pathway(s) and genes

Via an extensive literature review, we identified the Dissimilatory Nitrate Reduction to Ammonium (DNRA) pathway and the denitrification pathways, along with the operons that control them.

Identification of bacteria

We identified *Pseudomonas putida* Y-9, a bacterial strain that had been isolated from long-term flooded paddy fields in China. At the time, we believed this to be the only bacterial species in the world that could perform DNRA under aerobic conditions. *P. putida* Y-9 also performed denitrification under aerobic conditions.

Denitrification knockout

The first part of the plan was to delete the *nirK/nirS* genes responsible for the metabolic conversion of nitrate to nitric oxide. Stopping the expression of this operon would ensure that denitrification does not proceed, and DNRA would hypothetically automatically be preferred by the bacterium.

DNRA upregulation

Simultaneously, we also planned to upregulate the expression of DNRA. This would ensure that nitrate build-up did not increase up to and beyond its toxicity limit for the organism, while ensuring that ammonium was continuously churned out by the cell.

Build

Our first step was to reach out to the researchers who had discovered this novel organism. However, we could not get a response, no matter how desperately we tried to contact them. The genomic data of the organism were also not available online because it had not been fully sequenced yet.

Test

Due to our lack of resources, the testing phase in this cycle was geared toward testing the validity and feasibility of our idea.

We therefore spoke to Prof Ramesh Chand, chairperson of NITI Aayog, and Shyam Sundar Agarwal, Director of the Ministry of Chemicals and Fertilizers. Both are major stakeholders in the field of bioremediation and were interested in our problem statement and approach to the solution. They reinforced our hypothesis that nitrate leaching was a real, imminent problem and that researchers across the board were looking for viable solutions.

Learn

Characterization of gene dynamics:

DNRA being extremely understudied meant that we did not know which promoter gene was controlling its activity, and therefore did not know how it would respond to various environmental conditions. We learnt how to operate the NIH Genbank to search for other bacteria, at least the genes that were mentioned by name in the papers by the Chinese researchers. We then scoured the ATCC (American Type Culture Collection) and MTCC to locate what new bacteria were available to us.

While this was eventually a dead end, the unwieldy nature of the website inspired us to build a better tool with all the information within Genbank, but with a much better and user-friendly graphical user interface. We hope the tool will prove useful to biologists who may be as fed up with Genbank as we were.

Defining a knockout mechanism:

Gene deletion has quite a few standard procedures, like homologous recombination and conditional knockout. Our professors advised us to identify the method that would best work for our skill level and laboratory equipment. We quickly learned that knockout might not be a feasible option for us, because it was unstable and might not work as expected when dealing with an entire metabolic pathway. We had to reassess.

Building upon Team Cattlysts:

Team Cattlelyst from Wageningen University had already demonstrated the first step of the pathway, nitrate reduction to nitrite, in *P. putida* KT2440 during their own iGEM cycle. This was huge because it meant that we could work on perfecting only the nitrite reduction to ammonium pathway and could use their results to establish our proof of concept.

## Cycle 2

Design

Build

Test

Learn

Design

Isolation of new aerobic DNRA bacteria

After further digging, we stumbled upon an extremely recent paper that had isolated three more bacterial species that performed aerobic DNRA. *Bacillus salipaludis* PS3-36, *Neobacillus* sp. PS2-9, and PS3-12 had been isolated from another set of paddy fields, this time from Iksan city, Korea. These bacteria also performed DNRA under aerobic conditions, and were the first Bacillus species to carry out that pathway.

Next steps and challenges

Our next steps remained the same, wherein we would downregulate denitrification within the bacterium and upregulate DNRA. This meant our issues were also identical, with the largest problem being that downregulation was still a bit of a black box in terms of how we would actually carry it out.

Build

Since the genomes of these bacterial species were publicly available data, we rejoiced. After an appropriate amount of rejoicing, we got down to business and began to look into how we could obtain them.

We first tried and failed to get in touch with the researchers who had published the paper. Next, we scoured MTCC and ATCC to no avail. One of our secondary PIs, Professor Umesh Varshney, advised us to locate local genetic banks or microbial culture collections to see if they housed the strains.

Thus, we spoke to the curator at the MACS Collection of Microorganisms (MCM) at the Agharkar Research Institute in Maharashtra, India. They could, unfortunately, not help us because they did not house the strains either. They did, however, contain *Bacillus subtilis*, a common nitrifying bacterium. Prof Varshney expressed that it would be a good idea if we could work with *Bacillus subtilis* since his laboratory had previously worked with them, and the established protocols would prove extremely helpful to us during experimentation.

Test

Since we could not test whether *Bacillus subtilis* contained DNRA genes in the wet lab just yet, we turned to publicly available data. Our dry lab team assessed GEM models, which are networks of all the metabolites, enzymes, and corresponding genes involved within a bacterial species. Since *Bacillus subtilis* had a pre-established GEM model, we ran experiments to check whether it could theoretically perform DNRA, and whether it was a good idea at all to even try and procure the strain. We did not obtain conclusive evidence through these trials.

Furthermore, we extensively used the BLAST tool from Genbank to compare the genes that carried out this aerobic DNRA pathway in the Bacillus and Neobacillus species with pre-established anaerobic DNRA carried out by other microbes. To our utter surprise, the proteins produced were identical.

Learn

Breakthroughs

**The rise and fall of Bacillus:**

While we were initially extremely excited about these novel bacteria and the fact that *Bacillus subtilis* was a regular in our professor’s laboratory, we quickly stumbled upon a major trade-off. Bacillus species are notorious for forming spores. Considering that our eventual plan was environmental deployment, this would present a major biosafety challenge. While dismantling the spore-forming properties of the bacteria via gene editing would be possible, it would create another layer of uncertainty that we were ill-equipped to deal with.

**The Other Genes:**

While we had mostly been focused on *nirBD* as the genes responsible for nitrite reduction to ammonium, the *nrf* operon emerged during literature review. This operon existed as two variations across bacterial species: *nrfABCDEFG* and *nrfHAIJ*. For greater detail, please refer to the Design page. Moving forward, we have considered all three genes in our experimentation and further DBTL cycles.

**The breakthrough, and our solution to the aerobicity problem:**

Our biggest hurdle so far has been the difficulty in obtaining these novel and exotic strains, something we describe in detail in our blog post. Researchers were unresponsive, and it seemed that the elusive property of these bacteria to perform DNRA aerobically would elude us forever. However, the tests we ran by BLASTing proteins and nucleotide sequences together gave us the connecting link needed to identify the research gap in this domain.

1. We could decouple the operon from its promoter gene and attach it to a constitutive promoter of our choice instead. This would allow non-DNRA performing organisms, or organisms that only perform DNRA in anaerobic environments, to perform DNRA aerobically. We could create our own novel bacterium and tweak it to work under our own conditions. These revelations are largely credited to former iGEM participant and our senior, Aditya Kamath Ammembal, who helped us connect these dots.

2. We could disregard denitrification entirely. If we were to engineer non-DNRA bacteria and introduce this metabolic pathway into an ecosystem that previously did not contain it, along with the assurance that the proteins were stable enough to carry out their function, we would not need to suppress denitrification to reduce nitrogen loss from the soil.

3. In this period, we spoke to Dr. Priyanka Jamwal, researcher at ATREE (Ashoka Trust For Research In Ecology And The Environment). She agreed with the severity of the problem and directed us to groundwater pollution, an avenue we hadn’t considered yet. Her input on soak pits, source pollution, and rural groundwater dependence provided great context within which we grounded our project moving forward.

## Cycle 3

Design

Build

Test

Learn

Design

We decided to introduce the genes into a non-DNRA-performing bacterium via a plasmid. With our breakthrough, we could now bypass the aerobicity problem and also use a much more easily accessible bacterial species. This bacterium of choice turned out to be *Pseudomonas putida* KT2440, a microbial workhorse and robust soil bacterium.

Choice of chassis

One of the reasons we picked this chassis was because a previous team at our institute had also used *P. putida* KT2440 for their project, and had received it from Prof Prashant Phale at the Indian Institute of Technology, Bombay. We reached out to him, and his lab graciously sent us the agar stab containing the bacterium.

Plasmid selection

We decided to use plasmids from the SEVA repository because they were free and we were working with an extremely tight budget. Furthermore, the SEVA plasmids were well-characterized and easy to work with due to easy online access and sourcing. We also sourced *P. putida* EM42 from SEVA, which is a strain of KT2440 with over 200 inessential genes knocked out. This strain might therefore be more likely to take up external genes due to the lower level of intrinsic metabolic burden.

Build

We digitally modified our operons and added restriction enzymes to the ends for blunt-end restriction enzyme digestion. We were to receive the operons in clonal vectors from TWIST Bioscience, then digest them and ligate them into pSEVA expression vectors. We built digital maps of all our plasmids. All of the genes and vectors have been uploaded as BioBricks.

**Plasmid maps:**

*nrfABCDEFG* in pSEVA648
+

![Plasmid maps](https://static.igem.wiki/teams/6006/gallery/wetlab/nrfabcdefg.avif)

*nirBD* in pSEVA648
+

![Plasmid maps](https://static.igem.wiki/teams/6006/gallery/wetlab/pseva648-nirbd.avif)

*pSEVA648 map*
+

![Plasmid maps](https://static.igem.wiki/teams/6006/gallery/wetlab/pseva648.avif)

*pSEVA258 map*
+

![Plasmid maps](https://static.igem.wiki/teams/6006/gallery/wetlab/pseva258.avif)

*pSEVA238 map*
+

![Plasmid maps](https://static.igem.wiki/teams/6006/gallery/wetlab/pseva238.avif)

Test

When we cross-checked the amino acid sequences of our newly-built plasmids against the original protein sequences on Genbank, we found perfect matches. We were assured that our genes should be perfectly expressed. This enabled us to order the genes from TWIST Bioscience and prepare for the cycles ahead.

Learn

While this was a technical and highly specific issue, our operons were simply too large for TWIST Bioscience to synthesize in their entirety in the same plasmid. Therefore, we needed to split each of our operons so that they could fit within the synthesizable 5kbp limit.

Proof-of-concept in *E. coli*

Professors in the Microbiology and Cell Biology Department at our institute didn’t really work with *P. putida* KT2440. Therefore, we were advised to demonstrate our proof-of-concept in *Escherichia coli* DH5-Alpha, which would be easier to work with in our context.

Ramesh Vaidyanathan, a former student of one of our advisors, Prof Mahavir Sing, and current Business Development Manager at TWIST Biosciences, graciously provided input into how we could separately optimize our operons for expression in both bacteria using the Codon Adaptation Index theory.

Biofilm considerations & biosafety

We soon learned that *P. putida* KT2440 forms biofilms. Although biofilms are generally notorious for causing disease, *P.putida* KT2440 is actually a class of organisms that forms Plant Growth Promoting Biofilms (PGPB). These biofilms confer a multitude of benefits to the plant roots, as detailed in the project overview.

While deciding upon pSEVA plasmids, we stumbled upon multiple vectors with inducible promoter systems. At the All-India iGEM Meet, iGEM ambassadors conducted an enlightening workshop on biosafety, which planted the seed in our search for implementing some type of mechanism to ensure biosafety for our engineered organism. The biofilm-forming properties of the organism meant that the bacteria would be adhering to the plant roots, which inspired us to link the expression of the operon to an inducible promoter with a plant root exudate as the inducer.

SEVA plasmid challenges

We tried to import the vector pSEVA 248 (kanamycin-resistant) multiple times from the SEVA repository, but received an unviable bacterium *(E. coli)* every time. After three attempts, we reached back out to Prof Phale, who had a tie-up with SEVA. His lab had previously worked with them, and they graciously received the bacterium to send to us. Although they too failed in culturing the 248 bacterium, they did send us other SEVA plasmids with the same Origin of Replication and promoter system (XylS/Pm): pSEVA 238, 258, 648 (kanamycin-resistant), and the constitutive promoter pSEVA 2313 (gentamicin-resistant).

## Cycle 4

Design

Build

Test

Learn

Design

We now plan to use CAI to design the genes for optimum expression in both *E. coli* and *P. putida* separately. The operon would be working under an inducible promoter, and our inducer of choice was 2-methylbenzoate, a plant root exudate that is released in abundance by crops like maize and wheat.

Water bioremediation & biofilm framework

Simultaneously, we ideated a comprehensive system where our engineered microbe could be used in water bioremediation, wherein the final product would be ammonium-rich water that could be used in lieu of fertilizers, thus increasing fertilizer efficiency even further. Furthermore, we built a framework for the possible ways in which we could actually inoculate plant roots with the engineered bacterium for maximum biofilm formation, adhesion, and DNRA efficiency in terms of proximity to both 2-methylbenzoate and nitrate.

Build

We built the operons, courtesy of our sponsor Snapgene, and digitally inserted them into pSEVA vectors 238, 258, 648, and 2313. Having cross-checked amino acid sequences, we placed the order and received our operons from TWIST Bioscience.

TWIST Bioscience was not able to synthesize one-half of our operons due to manufacturing issues, meaning we now had two operons to work with instead of three. Therefore, we compiled protocols to isolate, digest, and ligate the operons with the pSEVA vectors before transforming *E.coli* DH5-Alpha.

We also built nitrite and ammonium testing assays for confirming DNRA in the transformed bacterium, and biofilm assays for confirming basal level and post-transformation biofilm-forming capabilities of *P. putida*.

Test

We isolated the plasmids from the shipping bacteria and digested them with our designed enzymes, then checked the DNA concentrations via a nanodrop machine. This process contained multiple DBTL cycles within itself, but we managed to obtain working concentrations for all DNA fragments multiple times. Find the details of how we troubleshooted issues during this phase in the Experimentation page.

We performed DNA ligation to connect the expression vector to the operons and transformed *E. coli*. Unfortunately, we did not see growth after our first transformation on Km agar plates. However, our dry lab modelled exceptional results, as detailed in the dry lab engineering webpage.

Our base-level tests for DNRA in *P.putida* started with optimizing the growth medium for *P. putida* KT2440 by testing different broth compositions from multiple sources. We then picked cultures with the highest growth and checked for base-levels of DNRA in *P. putida*, a test never before carried out with this specific bacterium, even though it does contain the nirBD genes that should cause production of nitrite reductase.

Learn

Plasmid kits can contaminate DNA samples with RNA and salts. To bypass this problem, we conducted manual plasmid isolations and compared the results as seen on the experimentation page.

Minimal growth media seemed to be the best for *P. putida* KT2440 growth, which was an interesting revelation considering literature consistently shows that a high C/N ratio is favored for DNRA. We discuss the implications on our experimentation page.

# Dry Lab

## Cycle 1

Design

Build

Test

Learn

Design

Large-scale model of eutrophication:

Based on our initial ideas, we had the idea to model on a large scale how eutrophication affects water bodies, using test cases of some of the rivers across the world that have been most hard hit by eutrophication due to nitrate leaching gone amok, and how our bacteria would curb and control these effects

We aimed to create an interactive UI that would depict how the selected water body would change over the years, with and without our project.

Biofilm modeling:

To study the microenvironment created within the biofilms, as one of our main concerns was that *P. putida* Y-9 is strictly aerobic, we sought to model the oxygen concentration of oxygen to see whether micro-oxic environments would be created, which would pose a huge problem to us!

GEM models

We came across GEM models, which are networks of all the metabolites, enzymes, and corresponding genes involved, and tried to use them as a base for modeling the reactions and kinetics of metabolite concentrations.

Build

Once we had figured out our ideas, we sought out to look for studies and literature on eutrophication, especially in the Indian subcontinent.

We looked into the literature of biofilm formation. Already existing tools and models which we could use.

We looked for GEMs of the *P. putida* bacteria family which may perform DNRA all in hopes of better understanding the metabolic activities of the organism in context to DNRA specifically. This also included learning how to work with the cobra.py library which is used to work with GEMs.

Test

Over time, *P. putida* Y-9 seemed to lead us to dead ends. With not many resources that would verify the claims of the original papers and no responses from the authors of the papers, we decided to shift our focus to a different microbe. We were not even able to find any genomic data of this strain, nor the amino acid sequence of the relevant proteins.

We kept testing and playing with GEMs of organisms that perform DNRA, varying the nitrate and ammonium content as input flux and inhibiting specific genes to see how it would affect bacterial growth and the output flux of ammonium.

Learn

While trying to model just the large-scale eutrophication of a lake over years, we realised that this would be far too complex a task for us to reasonably implement with our skills and the time we had, as we had to account for the complex interactions between agricultural soil, runoff water, groundwater, and rivers.

While moving through the project, this model becomes redundant, as by switching strains to *B. salipalidus*, these species can perform DNRA regardless of oxygen concentration.

While looking more into GEM models, and upon the inputs of Dr. Nagasuma Chandra, we realised that this line of work was irrelevant to our project, as GEM models provided no information on kinetics.

## Cycle 2

Design

Build

Test

Learn

Design

Soil modeling

After realizing the complexity of our goals from the first cycle, we decided to model soil dynamics around plant roots. We took into account the nutrient uptake rate of the plants, nitrite and nitrate leaching in vertical and horizontal directions, and variation with depth.

Pathway modelling

As we went through the literature on DNRA and ANRA pathways, we realised they are very poorly characterized. We were unable to find any kinetic characterization of the pathways at an enzymatic level, or the regulations on the production of involved genes either. So, we collected data on the rate of DNRA in different pH, nitrate, and ammonium concentrations in hopes of finding some expression for *nar* and *nir* enzyme expression.

Correlating various soil factors

To fit our model to real-world conditions, we needed to find how various soil factors like pH, nitrate and ammonium concentration, temperature, rainfall, latitude, and soil type relate with each other.

Genome comparison

To identify the relevant proteins in the newly discovered *Neobacillus* sp., we BLASTed the genomes of these three bacteria against other well-known and well-characterized species like *Bacillus subtilis*, to identify the enzymes and proteins relevant to biofilm formation. We further planned on using PGAP (Prokaryotic Genome Annotation Pipeline), a genome annotation tool by NCBI, to identify any relevant enzymes present in newly isolated soil bacteria from agricultural soil.

Protein modeling

We decided to model the different proteins obtained from using different restriction enzymes, since they added an extra terminal sequence of amino acids to the protein.

With the guidance of Dr. Mahavir, we decided to model the different proteins obtained from using different restriction enzymes, since they added an extra terminal sequence of amino acids to the protein. We did this to help us choose a restriction enzyme that would not interfere with enzyme structure and functionality. We modeled three structures of the proteins with all the various changes and compared the structure on AlphaFold.

Build

Upon trying to model the soil conditions, we once again realised that the complexity of this endeavor was very high, so we decided to abandon this.

While trying to characterise the kinetics and protein expression, at the guidance of Mr. Aditya, we realised due to several reasons that this would be much harder than anticipated, since we didn’t have the capabilities to isolate and study the enzymes and their expression in the lab. Furthermore, the discrepancy between the order of magnitudes of enzyme production and rate of change of metabolite concentration would pose more problems.

The BLAST comparisons of the Neobacillus species were widely successful, and we were able to successfully identify the exact subunits involved in the operons using the *B. subtilis* genome.

Test

These subunits had not been characterized by the research group that identified the species, but we identified the orthologous genes with very high accuracy. Upon confirmation with literature, we were able to confirm that the binding sites and active regions matched exactly with the prediction.
We also identified the presence of certain genes that are crucial in biofilm formation.

We modeled the different structures of proteins arising from the various choices of restriction enzymes on AlphaFold, and upon analysis and comparison, we realised that for our choice of restriction sites, there was negligible change in protein structure and functionality.

Learn

From our soil modelling attempts, we learned how the complexity of accurately simulating soil dynamics with root interactions and leaching was beyond our current scope and resources.

The pathway modelling attempts made us realize the pursuit is futile, given we had little existing enzymatic data and lacked proper resources to conduct our own experiments in that regard.

Through genome comparison tests, we could identify orthologous genes, confirm the active sites with literature, and discover uncharacterized biofilm-related genes.

From protein modeling results, we learned that the terminal amino acid additions from the chosen restriction enzymes did not affect the protein structure significantly. Thus, we decided to go forth with the most economical and easily available restriction enzyme for the wet lab.

## Cycle 3

Design

Build

Test

Learn

Design

Modelling genetically modified system

Upon realising the complexity, we decided to model the pathway in the bacterium where the enzymes are being expressed at a constant rate by a constitutive promoter. Another factor that pushed us to pursue modelling of the final bacterium over the chassis was the synergy it provides to the rest of our project, as doing this would allow us to predict the conversion rate under various conditions, according to which it can be applied.

Fixing GenBank UI

After using the GenBank framework extensively for BLAST comparisons, we realised that the tool is highly inefficient and very inconvenient to use, especially for someone from a traditional biology background with no experience. Hence, we decided to try to improve this framework to improve the ease for future iGEM teams.

Using ML models to find correlations

Upon realising the huge number of variables involved in soil conditions, we decided to use an ML approach to train on large data sets of soil conditions, based on which it would predict desired output.

Product scaling

Using the model to calculate the nitrate to ammonium conversion rates, we planned to use this to guide the commercial application of our GMO bacterium into soil, by fine-tuning the biomass of the bacterium and the amount of fertilizer that needs to be applied for certain soil types and plant species for optimal growth and minimal nitrogen loss.

Build

We began developing the model of the genetically modified bacterium, incorporating constant enzyme expression through a constitutive promoter. This helped us simplify the system and make it more tractable.

We started planning an improved version of the GenBank framework with a more user-friendly interface to assist future users, especially those without strong computational backgrounds.

For the ML model, we began identifying publicly available soil datasets that included variables such as pH, temperature, rainfall, and nitrogen content to serve as training data.

In preparation for scaling, we conceptualized how the predictive model could be integrated with real-world soil data to generate fertilizer optimization recommendations.

Test

Initial testing focused on simulating the metabolic pathway of the modified bacterium to verify that constant enzyme expression provided stable conversion rate predictions under different conditions.

We benchmarked the GenBank system to identify the most critical UI components needing improvement. We decided to focus on the BLAST framework.

Preliminary ML runs were planned to evaluate the model’s ability to correlate key soil parameters and predict nitrate-to-ammonium ratios.

![MSE loss vs Epoch](https://static.igem.wiki/teams/6006/wiki/misc/unnamed3.avif)

The framework for product scaling was tested conceptually by assessing if model outputs could meaningfully inform bacterial biomass and fertilizer input ratios for different soil types.

Learn

From the theory on genetically modified bacterium circuits, we learnt that simplifying enzyme expression to a constant rate made pathway simulations much more manageable while maintaining biological relevance.

From evaluating the GenBank framework, we confirmed that improving its interface could greatly enhance usability for biologists without computational training.

Upon inputs from our senior Armaan Khetarpaul, we decided to use XGBoost models instead of neural networks, since they are less prone to overfitting.

From the product scaling concept, we learnt that the combination of pathway modelling and soil condition predictions could directly guide practical applications, improving both efficiency and sustainability.

## Cycle 4

Design

Build

Test

Learn

Design

Soil ML

We started building the XGBoost model, and applied a log(1+x) transformation, which stabilizes variance and improves model learning on our ammonium data, which was positively skewed data. Simultaneously, once again at the suggestion of our senior Armaan Khetarpaul, we decided to use synthetic data to create data sets for the ammonium concentration in soil after our bacterium is applied.

ODE model

We decided to build the model similar to the previous iGEM teams, Cattlelyst and Halocleen, using a system of ODEs, characterising the concentrations of metabolites inside the cell and in the surrounding medium. We accounted for transport reactions between these two compartments and assimilation of intracellular ammonium by the GS/GOGAT cycles. Later on, we also incorporated the leaching of extracellular nitrate and the uptake of extracellular ammonium and nitrate by plants.

Genbank UI

Our goal was to modernize and simplify working with NCBI’s GenBank. Addressing issues like poor search accuracy, lack of context explanations, and weak visualization options. We created a list of pre-existing biotech tools online and consulted bioengineering enthusiasts and faculty about problems with these resources.

Build

Soil ML

We used an XGBoost Regressor with hyperparameters tuned through a previous search, with our chosen configuration emphasizing balanced learning with moderate regularization. We applied a smearing estimate (a standard bias correction technique) when converting predictions back to the original scale. To generate the synthetic data, we used our ODE model and literature to predict by what percentage the rate of DNRA will increase in the soil.

ODE model

We built the model on COPASI software, drawing inspiration from the previous iGEM team Cattlelyst from Wageningen 2021, to construct the ODEs and obtain some of the rate constants. We obtained the other values from previous characterisations of the enzymes from the literature. We later expanded the model to include nitrate leaching and N uptake by plants at a constant rate.

Genbank UI

We created a preliminary framework in Python, learnt to bypass the use of Pio-python.

Test

Soil ML

We evaluated the model on the test set using standard regression metrics on the original ammonium scale. The results obtained were pretty good considering the small data size, explaining almost 60% of the variation in ammonium levels, meaning that the model is picking up on many important patterns.

After going through the literature and simulating conditions on our ODE model, and running the ML model on some different conditions, we finally settled on a percentage increase value obtained by comparing the maximum conversion rates in our model and some rates from the literature and the previous iGEM team Wageningen.

ODE model

We had several testing phases by changing constant values and observing conversion rates, and noticed that there were two issues initially: unrealistically high cell growth and accumulation of ammonium in the cellular component. We fixed these issues by capping the cell growth at a fixed value and by restricting ammonium import beyond a certain concentration of cellular ammonium.

We also varied the enzyme concentration across various values and observed the cellular nitrate values, which were all within permissible values and would not cause toxicity. While varying the rate of enzyme concentration, we also observed a wide range of conversion efficiencies from about 10% to 98%, and at very high concentrations (>M) of enzyme, the model broke down.

Upon including the leaching and uptake, and varying the values and observing efficiency,y and by comparing with real-world values, we decided that the most sensible approximation for the rate would be 10-11mol/L. We also switched around enzyme concentrations after this inclusion and observed that in some ranges the change in efficiency is drastic, but in others there was barely any change.

Genbank UI

For testing, we presented our solutions to said-tool users for their feedback and insights on how to improve on it. We optimized the code in JavaScript and built a GitHub website to host our tool.

Learn

Soil ML

We realised that the dataset being quite small, with only 902 samples, likely limits the model’s performance. More data would help it learn more nuanced patterns and improve accuracy further. We believe that future iGEM teams should focus on expanding the dataset with more diverse soil samples and conditions. This would allow for training more robust models that can better capture the complexity of soil ammonium dynamics.

ODE model

From the huge amount of testing done, we figured out the limits of the model and what are the realistic ranges of enzyme concentration, cell volume, uptake, and leaching value, which are vital for being able to predict optimal conditions for given conditions of soil and plant type. This testing helped us understand the potential utility of our model in designing Wet Lab experiments, by constraining certain factors such as the ratio of nitrate: nitrite reductase to ensure no nitrite build-up. It also helps us better characterize the functionality of the modified bacterium so that we at least know a ballpark estimate of what the expected results should be.

Genbank UI

We hope the tool we developed can be of use to people in the field in some way. We were able to learn a lot of new things about how things actually work behind the simple webpage that we see. But most importantly, the development of GenBankUI was a fun way to interact with our juniors, who helped us with the development of this tool! We could familiarize them with the world of synbio and iGEM.

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## What Should this Page Contain?

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Provide comprehensive, step-by-step protocols for all experiments conducted. Include a list of materials, reagents, and equipment used. Specify concentrations, volumes, incubation times, temperatures, and other critical parameters. Document any modifications or optimizations made to standard protocols.

Explain the purpose of each experiment and its relevance to your project goals. Describe the experimental design, including controls and replicates. Provide a clear rationale for the chosen methods and approaches.

Document any troubleshooting steps taken and optimizations made during the experimental process. Share any lessons learned that could benefit future teams.

## Inspirations

---

* [2024 Heidelberg](https://2024.igem.wiki/heidelberg/experiments)
* [2024 Marburg](https://2024.igem.wiki/marburg/experiments)
* [2024 EPFL](https://2024.igem.wiki/epfl/experiments)
* [2023 Estonia-TUIT](https://2023.igem.wiki/estonia-tuit/experiments)

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# Perception of GMO use in Agriculture

Understanding Public and Farmer Perspectives on Genetically Modified Organisms

## Surveying the Public

ARGUS-2440 was shaped by continuous feedback from our stakeholders - farmers, industry experts, researchers, and government officials. Now, it was time to present our work to a wider demographic. A hallmark of good science communication is that the content is tailored to the audience. This begs the question: what pre-existing knowledge does the public possess about our project themes? It was time for our first survey.

**Survey Overview:** The survey collected 87 responses over four days. 93% of the survey participants are pursuing/have completed Bachelor's degrees. In fact, 38.4% are pursuing/have completed higher (postgraduate) education.

![Level of education](https://static.igem.wiki/teams/6006/wiki/surveyspics/p1.avif)

Figure 1: Educational Background of Survey Participants

Around three-fourths of the participants believed, based on prior knowledge, that agricultural practices are unsustainable. This reflects the severity of the issue. While our project specifically deals with nitrate leaching, there are many more demons to be fought. The impact of unsustainable practices is amplified in India compared to other countries, since we are an agrarian nation.

✓ 72.1% of participants were aware of nitrate leaching
✓ 67.4% aware of detriments of excess nitrates in drinking water

Thus, there is decent awareness about our problem. This was great news for us.

![Sustainability of agricultural practices](https://static.igem.wiki/teams/6006/wiki/surveyspics/p2.avif)

Figure 2: Views on Sustainability of Agricultural Practices

![Nitrate leaching](https://static.igem.wiki/teams/6006/wiki/surveyspics/p3.avif)

Figure 3: Awareness of Nitrate Leaching

![Impact of excess nitrates on health](https://static.igem.wiki/teams/6006/wiki/surveyspics/p4.avif)

Figure 4: Knowledge of Health Effects from Excess Nitrates

88.4% of participants knew what Genetically Modified Organisms (GMOs) are, and the rest were hopefully enlightened by a document that we attached. Now that everyone had at least a basic understanding of the term 'GMO', we could ask for their opinions on deploying them for agricultural uses. BT Brinjal and BT Cotton are quite famous, so the natural next question was about the participants' views on GM plants. **Almost 90% of survey takers voted in their favor, although half of them believed that the crops that we consume should not be genetically modified.**

![Knowledge about GMOs](https://static.igem.wiki/teams/6006/wiki/surveyspics/p5.avif)

Figure 5: Knowledge About GMOs

![Commercialization of GM crops](https://static.igem.wiki/teams/6006/wiki/surveyspics/p6.avif)

Figure 6: Views on Commercialization of GM Crops

**Why should one support GM crops?**

"Organisms are exposed to constitutive sources of mutations that alter their genome, often irreversibly, and these mostly go unnoticed while we consume them. So, on anthropologically modifying them, it just does not really change anything."

"GM crops have been demonized unnecessarily. When tested and approved on a large scale for safety for both consumption/use and environmental impact, GM crops are actually very helpful. They are mostly seen in a negative light due to a campaign of fearmongering and also due to their unfortunate role in securing seed monopoly for US companies, who used this technology to copyright-protect literal seeds and their genetic design so no can grow them independently after buying them once. If those issues can be solved, I believe that GM crops can be a boon for many, especially vegetarians and vegans."

"A technology should not be deemed bad solely by virtue of it being bioengineered and not found in nature. Even modern crops are technically only results of generations of primitive selective farming procedures by humans to cultivate better crops by selecting for traits advantageous for human consumption. Extrapolating this to the genetic level is not a huge leap, except if it is done on the basis of nefarious intentions by corporations (e.g., the allegations against Monsanto regarding the use of its pesticide-resistant seeds). It is entirely possible though that the early adoption cost might be high, but over the long run crops cultivated with such GM techniques might end up being more productive. Genetic modifications to plants can help increase the productivity of the land by producing greater yield and lesser waste by breeding for traits like increased caloric amount, better taste, resistance to disease, etc. It is simply a tool that can be utilized for good, and its harms lie more in how they are developed and distributed than in the scaremongering regarding adverse health effects."

**Alternatively, why should one not support GM crops?**

**Concerns raised:**
• Monoculture of genetically similar crops increases risk of diseases wiping out entire fields
• GMOs are produced in limited locations and not adapted to local climate and nutrient availability
• Control of seeds should be with farmers, not with foreign companies

"While I do support GMOs, I don't think the current systems in India can prevent malpractices and corporate exploitation of this area. So I don't think we should be rolling out large-scale adoption of GMOs yet."

"We are steadily losing everything that is natural and organic. Growing manufactured food and calling it natural, healthy and better is incorrect. In many ways it affects directly not just the quality of the food but also our health. Traditional method of farming, seeds that have survived the test of time and natural pest control methods are the way forward."

✓ 67.8% support using GMOs to aid crop growth
✓ 26.4% open to considering the idea with more information

**What's stopping them?**

**Key Concerns:**
• Ecological consequences of releasing bacteria with new genes into soil communities
• Potential changes in soil conditions and effects on carbon cycling
• Ownership of bacteria and farmer autonomy in cultivation
• Lack of long-term risk information
• Understanding of GMO containment mechanisms

![Non-GMO agricultural produce](https://static.igem.wiki/teams/6006/wiki/surveyspics/p7.avif)

Figure 7: Preference for Non-GMO Produce

**Major Insight:** Of the people who were not fully on board with the idea of GMO aids, **96.8% said that they would change their answer if said aid enhanced sustainability or crop yield, or both.** In fact, 65.8% of participants would even pay more for such a product, showing that the public is accepting of GMO use, given the certainty that it is safe.

However, around half of the people believe that the price hike should not exceed 10%. This is understandable, since not everyone is economically positioned to take surging prices in stride. Produce must be affordable – both for the farmers (in terms of cultivation costs) as well as the consumers. Furthermore, 75.9% participants believe that increasing the price of GMO-aided produce is justified if it reduces health risks in rural communities, the main victims of nitrate leaching.

![Beneficial soil GMOs](https://static.igem.wiki/teams/6006/wiki/surveyspics/p8.avif)

Figure 8: Acceptance of Beneficial Soil GMOs

![Higher cost for sustainable produce](https://static.igem.wiki/teams/6006/wiki/surveyspics/p9.avif)

Figure 9: Willingness to Pay for Sustainable Produce

![Percentage increase in pricing](https://static.igem.wiki/teams/6006/wiki/surveyspics/p10.avif)

Figure 10: Acceptable Price Increase Range

![Justification of price hike](https://static.igem.wiki/teams/6006/wiki/surveyspics/p11.avif)

Figure 11: Support for Price Increases for Health Benefits

**Key Conclusion:** Much of the wariness surrounding GMOs comes from a lack of information about their behaviour and impact. Should the organism be rigorously tested and proven safe for deployment, people will be more welcoming towards it. Of course, there are always concerns that such artificial selection is not 'natural' and that it may not gel well with pre-existing flora and fauna. However, mankind has always been tailoring its surroundings to favor its interests. The question is: are we wielding this power with safety, responsibility, and accountability?

We closed the survey by inviting questions and criticism about our project. Thank you to everyone who took the time to fill out the survey! Your responses are valuable to us.

## Surveying the Farmers

Continuing our series of interactions with our primary stakeholders, we surveyed to answer the million-dollar question: Would they be willing to use our product? Now, GMO-based crop growth aids are at the far end of the spectrum of agricultural products that farmers may be open to trying – and not necessarily because of the novelty. Farmers do not have the luxury of simply trying new products, for they cannot afford any losses from the changes they need to make to accommodate the product.

**Survey Design:** We designed the questionnaire in Marathi and circulated it through Radhika Ghate and Utkarsh Ghate – relatives of our team lead, Aarav Ghate. The survey gathered 11 responses from farmers of various districts – Pune, Latur, Raigad, Sangli, Ranchi, and Uttar Pradesh. Participants grow a variety of crops with soybean and rice being the most popular, both requiring high amounts of nitrogen.

![Distribution of crop types](https://static.igem.wiki/teams/6006/wiki/surveyspics/f1.avif)

Figure 12: Crop Types Grown by Farmers

Next, we asked them about what kind of fertilizers they use. An overwhelming majority of the participants use Suriya and manure – organic fertilizers. This is a great sign as organic fertilizers are environmentally friendly and sustainable.

![Distribution of fertilizer types](https://static.igem.wiki/teams/6006/wiki/surveyspics/f2.avif)

Figure 13: Fertilizer Types Used by Farmers

✓ 100% of farmers confirmed their fertilizers are effective and contain nitrogen
✓ 63.6% have observed fertilizers washing away due to irrigation and rainfall

✓ 100% aware of negative effects of nitrate-leaching on water sources
✓ 81.8% know of people/animals falling ill from consuming contaminated water

This confirms that rural communities are heavily impacted by nitrate pollution.

![Fertilizer flushing](https://static.igem.wiki/teams/6006/wiki/surveyspics/f3.avif)

Figure 14: Observation of Fertilizer Flushing

![Impact of fertilizer leaching on health](https://static.igem.wiki/teams/6006/wiki/surveyspics/f5.avif)

Figure 15: Health Impact Awareness

✓ 90.9% of participants take responsibility for environmental protection through agriculture

"As farmers, it is our responsibility to ensure that our actions have no severe consequences."

A proposed method of application of our bacterium is coating the seeds with the inoculum before sowing them. For farmers to accept this product, perhaps they can be given a few free samples. Since cost is a major factor, the seeds need to be cheaper than existing seeds or the same price.

**Free Product - Environmental Benefit:**

✓ 63.6% said they would try a free product that benefits the environment
✗ 36.4% were hesitant, perhaps because it is new and doesn't benefit them directly

![Environmentally beneficial free product](https://static.igem.wiki/teams/6006/wiki/surveyspics/f6.avif)

Figure 16: Willingness to Try Free Eco-Friendly Products

**Purchasing Scenario - Environmental Benefit:**

As expected, fewer farmers said yes to purchasing the same product. Numbers also shifted from unsure to not very likely and not likely, since farmers often hire others to spray fertilizers and associated costs may rise.

![Purchasing environmentally beneficial product](https://static.igem.wiki/teams/6006/wiki/surveyspics/f7.avif)

Figure 17: Willingness to Purchase Eco-Friendly Products

**Seed-Coated Product - Pricing Scenarios:**

**Important Finding:** If it were a seed coating product, **81.8% of people said they would be likely to try it out if it were cheaper than existing seeds.** This provides valuable insight for us. A seed-coat model might work better than a spray model from a marketing perspective.

![Coated seeds cheaper than regular seeds](https://static.igem.wiki/teams/6006/wiki/surveyspics/f8.avif)

Figure 18: Adoption of Cheaper Coated Seeds

If the coated seeds were the same price as the seeds on the market right now, the distribution of responses changed to a roughly 50/50 split between maybe and maybe not.

![Coated seeds the same price as regular seeds](https://static.igem.wiki/teams/6006/wiki/surveyspics/f9.avif)

Figure 19: Adoption of Same-Price Coated Seeds

**Crop-Protective Product:**

✓ 66.7% of farmers willing to try a product that protects crops and increases yield (free version)
✗ 33.3% hesitant without elaborating on reasons

![Plant protecting free product](https://static.igem.wiki/teams/6006/wiki/surveyspics/f10.avif)

Figure 20: Interest in Crop-Protective Products

Following a similar pattern as before, the majority were unsure upon considering the possibility of purchasing the product.

![Purchasing plant protecting product](https://static.igem.wiki/teams/6006/wiki/surveyspics/f11.avif)

Figure 21: Purchase Intent for Crop-Protective Products

✓ 72.8% participants likely to purchase coated seeds if cheaper than existing ones

![Coated seeds cheaper than regular seeds](https://static.igem.wiki/teams/6006/wiki/surveyspics/f12.avif)

Figure 22: Purchase Intent for Cheaper Crop-Protective Seeds

Next, if the coated seeds and regular seeds were of the same price, most participants would still consider them! This is a step up from the previous non-profitable variant, albeit the 'likely' responses are significantly more than the 'very likely' ones, indicating slight uncertainty.

![Coated seeds the same price as regular seeds](https://static.igem.wiki/teams/6006/wiki/surveyspics/f13.avif)

Figure 23: Purchase Intent for Same-Price Crop-Protective Seeds

**GMO Awareness and Perception:**

ℹ Only 1 farmer was aware of using GMOs in agriculture

![Awareness regarding GMO use in agriculture](https://static.igem.wiki/teams/6006/wiki/surveyspics/f14.avif)

Figure 24: GMO Awareness Among Farmers

![Effect of the presence of GMOs on previous answers](https://static.igem.wiki/teams/6006/wiki/surveyspics/f15.avif)

Figure 25: GMO Status Impact on Purchasing Decisions

Farmers expressed that either their answers would not change even if the products from the previous questions contained GMOs, or that they cannot comment without possessing knowledge about them.

**Farmer Insights on Adoption:**

"One will have to provide farmers with a lot of information regarding this."

"Seeds prepared by research through Krushi Vigyan Kendras are faultless. They are good to use."

"Farmers tend to favor old practices over new fertilizers and seeds. Manure is extremely beneficial for farmland."

"Farmers try a new product once and purchase it for a second time if it is useful. If not, they look for other alternatives."

**Final Conclusion:** There is hope, but we have a long way to go. Perhaps with enough awareness and marketing, a GMO-based crop aid can break into the market. The key is demonstrating tangible benefits – whether through improved yields, reduced costs, or environmental protection – while building trust through transparent communication.

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