iGEM 2020 — Team MichiganState

[![](https://static.igem.org/mediawiki/2020/8/8b/HQ_page_logo.jpg)](https://2020.igem.org)

# Team:MichiganState/Notebook

[![](https://static.igem.org/mediawiki/2020/f/fa/T--MichiganState--BeeToxLogo.png)](https://2020.igem.org/Team%3AMichiganState)

Judging

[Judging Form](https://igem.org/2020_Judging_Form?id=3359)

[Safety](https://2020.igem.org/Team%3AMichiganState/Safety)

Awards

[Education](https://2020.igem.org/Team%3AMichiganState/Education)

Human Practices

[Human Practices](https://2020.igem.org/Team%3AMichiganState/Human_Practices)

[Integrated Human Practices](https://2020.igem.org/Team%3AMichiganState/Human_Practices/Integrated)

Project

[Contribution](https://2020.igem.org/Team%3AMichiganState/Contribution)

[Description](https://2020.igem.org/Team%3AMichiganState/Description)

[Design](https://2020.igem.org/Team%3AMichiganState/Design)

[Engineering](https://2020.igem.org/Team%3AMichiganState/Engineering)

[Experiments](https://2020.igem.org/Team%3AMichiganState/Experiments)

[Notebook](https://2020.igem.org/Team%3AMichiganState/Notebook)

[Implementation](https://2020.igem.org/Team%3AMichiganState/Implementation)

[Results](https://2020.igem.org/Team%3AMichiganState/Results)

Team

[Team](https://2020.igem.org/Team%3AMichiganState/Team)

[Attributions and Sponsors](https://2020.igem.org/Team%3AMichiganState/Attributions)

[Collaborations](https://2020.igem.org/Team%3AMichiganState/Collaborations)

# Lab Notebook

![notebook paper](https://static.igem.org/mediawiki/2020/1/12/T--MichiganState--notebook_paper.png)
![test tube](https://static.igem.org/mediawiki/2020/4/45/T--MichiganState--test-tube2.png)

[Bioinformatics](https://2020.igem.org/Team%3AMichiganState/Notebook/Bioinformatics)
[Device Design](https://2020.igem.org/Team%3AMichiganState/Notebook/Device_Design)
[Gene Engineering](https://2020.igem.org/Team%3AMichiganState/Notebook/Gene_Engineering)

#### **The project was divided into three subgroups. Click to see what each subgroup has been doing daily.**

* [![Instagram](https://static.igem.org/mediawiki/2020/5/5b/T--MichiganState--IGIcon.png)](https://www.instagram.com/msuigem/)
* [![Twitter](https://static.igem.org/mediawiki/2020/a/ae/T--MichiganState--TwitterIcon.png)](https://twitter.com/msuigem)
* ![Email](https://static.igem.org/mediawiki/2020/a/ac/T--MichiganState--Emailicon.png)

[![](https://static.igem.org/mediawiki/2020/8/8b/HQ_page_logo.jpg)](https://2020.igem.org)

# Team:MichiganState/Engineering

[![](https://static.igem.org/mediawiki/2020/f/fa/T--MichiganState--BeeToxLogo.png)](https://2020.igem.org/Team%3AMichiganState)

Judging

[Judging Form](https://igem.org/2020_Judging_Form?id=3359)

[Safety](https://2020.igem.org/Team%3AMichiganState/Safety)

Awards

[Education](https://2020.igem.org/Team%3AMichiganState/Education)

Human Practices

[Human Practices](https://2020.igem.org/Team%3AMichiganState/Human_Practices)

[Integrated Human Practices](https://2020.igem.org/Team%3AMichiganState/Human_Practices/Integrated)

Project

[Contribution](https://2020.igem.org/Team%3AMichiganState/Contribution)

[Description](https://2020.igem.org/Team%3AMichiganState/Description)

[Design](https://2020.igem.org/Team%3AMichiganState/Design)

[Engineering](https://2020.igem.org/Team%3AMichiganState/Engineering)

[Experiments](https://2020.igem.org/Team%3AMichiganState/Experiments)

[Notebook](https://2020.igem.org/Team%3AMichiganState/Notebook)

[Implementation](https://2020.igem.org/Team%3AMichiganState/Implementation)

[Results](https://2020.igem.org/Team%3AMichiganState/Results)

Team

[Team](https://2020.igem.org/Team%3AMichiganState/Team)

[Attributions and Sponsors](https://2020.igem.org/Team%3AMichiganState/Attributions)

[Collaborations](https://2020.igem.org/Team%3AMichiganState/Collaborations)

![](https://static.igem.org/mediawiki/2020/2/20/T--MichiganState--submenuIcon.jpeg)

[Engineering Success](#Eng)
[Gene Engineering](#GE)
[Bioinformatics](#Bio)
[- Transcriptomics Experimental Design](#Tomics)
[- Metabolomics](#Momics)
[- Multi-omics Approach](#Multiomics)
[References](#Ref)

# Engineering Success

![](https://static.igem.org/mediawiki/2020/8/8b/T--MichiganState--DNA_to_function.jpg)

# Gene Engineering

## Introduction

In order to accomplish our goal of aiding bees in the breakdown of harmful pesticides like imidacloprid, we decided to introduce a detoxification enzyme into the bee gut microbiome. We needed to first find an organism that would establish in the bee gut and then design a secretion system that could export our enzyme into the bee gut where it could detoxify pesticides.

## Research

In our research, we discovered the Bee Microbiome Toolkit [1], which designed and tested vectors and promoters that could be used to genetically engineer bacteria of the bee microbiome. This was done by Nancy Moran and her team at the University of Texas Austin. We found that Moran’s team had success genetically engineering the bee gut symbiont, *Snodgrassella alvi*. *S. alvi* is a gram-negative, microaerophilic bacterium located in the hindgut of bees. They were able to express RNAi constructs in *S. alvi*, which silenced bee genes when the recombinant *S. alvi* established in the bee gut [2]. This inspired us to use *S. alvi* as our chassis organism, as we too wanted to be able to express a construct in a microbe that could establish in the bee gut. From the Bee Microbiome Toolkit, we discovered the vectors, promoters, and CRISPR system that was successful in *S. alvi*. Once we decided we could use *S. alvi* to produce a detoxification enzyme for the bee, we needed a way for the enzyme to leave the bacterial cell and be available in the bee hindgut. We researched many different secretion systems that would allow us to accomplish this. We decided to use a Type 1 secretion system, as these systems span the entire membrane of gram-negative bacteria and secretes specific, tagged proteins into the extracellular space in one step [3].

We decided to integrate the HlyA secretion system into our plasmid which uses a secretion tag attached to the carboxyl terminus to secrete HlyA, a blood-lysing enzyme. HlyB is an ABC transporter located in the inner membrane, HlyD is a membrane fusion protein anchored in the inner membrane that spans across the periplasm, and TolC is an outer membrane protein. HlyB interacts with the C-terminus attached to the HlyA gene and recruits the other membrane proteins to form a continuous channel that HlyA will be secreted out of [4].

## Imagine

We next visualized how we would construct this plasmid to incorporate the three HlyA secretion system proteins, HlyB, HlyD, and TolC. Before deciding on an enzyme to secrete we wanted to imagine what secretion would look like using this system, so we decided to use mRFP, a slow-folding fluorescent protein, in order to visualize secretion and determine the effectiveness of our secretion system. The slow-folding nature of mRFP was important, as slow-folding proteins are typically secreted by Type 1 secretion systems. The mRFP gene linked to the HlyA C-terminus secretion tag, HlyB, HlyD, and TolC would all be added to our vector. We decided to use the vector pBTK510 from the Bee Microbiome Toolkit, as it was found to be the most successful plasmid in *S. alvi*.

We also researched various biocontainment mechanisms that could be engineered into our construct in the future, if BeeTox were to be administered to bees out in the field. More information about this can be found on our [Proposed Implementation page](https://2020.igem.org/Team%3AMichiganState/Implementation). Since our plans for biocontainment involve using CRISPR to integrate a gene onto the *S. alvi* chromosome, we knew we wanted to test genome integration of a gene and its expression, as this has not been documented before in *S. alvi*. We imagined that we would use CRISPR to deliver GFP to the *S. alvi* chromosome. We could test the expression of this integrated gene by measuring and quantifying fluorescence.

![](https://static.igem.org/mediawiki/2020/7/79/T--MichiganState--secretion_system_engineering_success.png)

Figure 1: This is a schematic diagram of how we imagined to test the secretion system in *S. alvi*.

## Design

We began designing our constructs using Benchling for use in our experiments. The HlyB, HlyD, and TolC genes combined are very long, so we had to break them up and add them to our vector separately, as opposed to transferring them all at once via a gblock. We first designed a gblock with mRFP (linked to the secretion C-tag) and TolC. A restriction site was introduced at the end of TolC, where the HlyB/HlyD construct will be inserted. We planned to amplify out the HlyB and HlyD genes from the iGEM 2019 Distribution Kit, which had the genes linked together on a construct. To design our chromosomal integration system to introduce GFP into *S. alvi*, we first found a place on the *S. alvi* chromosome which would not interrupt the function of other genes so we could integrate the new gene. We chose an open space on the genome that did not contain any genes, and was in between two other genes, so there was less of a chance of an unannotated gene being disrupted by our gene insertion. We designed a gblock that contained the gRNA targeting the PAM site on the chromosome where Cas9 will make a cut, as well as the homology regions that correspond to the chromosome where we will insert the GFP. We added a mutated PAM site to the gblock so the Cas9 will not re-target the mutated genome. In between the homology regions of the gblock, there is a restriction cut site into which we will insert GFP to have a complete replacement cassette of gRNA and homology regions flanking GFP that will be inserted.

## Build

After designing all of our constructs in Benchling we started planning how to assemble all of our constructs into the appropriate vectors. For the secretion plasmid, we planned to first use PCR to amplify the HlyB/HlyD part from the Distribution Kit. We then planned to perform Gibson Assembly to insert the mRFP/TolC gblock into the pBTK510 plasmidt. Next, we would do a restriction digest with the assembled plasmid we had just created, as well as Gibson Assembly to insert the HlyB/HlyD construct. After this, we will have a completed vector with mRFP, TolC, HlyB, and HlyD.

![](https://static.igem.org/mediawiki/2020/7/71/T--MichiganState--EngineeringSuccess_GE_Fig2.jpg)

Figure 2: Here is the final secretion system plasmid that will be created, after following the steps above.

To build our vector for the genome integration, we will first perform a Gibson Assembly with our gRNA gblock to add it to the pBTK599, a suicide vector from the Bee Microbiome Toolkit used for genome integration. We will then perform a restriction digest and subsequent Gibson Assembly to insert the GFP gene in between the homology regions. This will result in a complete replacement cassette plasmid with the gRNA and GFP gene with flanking homology on the suicide vector.

![](https://static.igem.org/mediawiki/2020/2/27/T--MichiganState--EngineeringSuccess_GE_Fig3.jpg)

Figure 3: This is the assembled sucide vector, pBTK599, with our replacement cassette.

## Test

To test our secretion system, we would first transform our plasmid into *E.coli* donor strain WM6026. We would then perform a conjugation to transfer the plasmid into *S. alvi*. Once the plasmid is confirmed to have been established in *S. alvi* we would separate the cells from the supernatant to test the supernatant for the secreted mRFP. This would be done using a plate reader to measure the fluorescence compared to a standard dilution curve. To test our genome integration experiment, we would first transform the suicide vector into the *E.coli* donor strain, then conjugate it into *S. alvi*. We would then perform a second round of transformation and conjugation to add the pX2-Cas9 plasmid that contains the Cas9, which will make the cut at our target location of the genome. We will confirm the integration of the GFP gene into *S. alvi* with PCR. We would then measure and quantify the fluorescence of the cells using a plate reader. To read our detailed protocols on the different aspects of our experiments, see our [Experiments page](https://2020.igem.org/Team%3AMichiganState/Experiments).

## Learn and Improve

We were unable to perform experiments in the lab this summer due to COVID- 19 restrictions. Our graduate student mentor, Kati Ford, started the assembly and transformation experiments, but unfortunately, was unable to obtain results from them. One of the challenges she encountered was that the mRFP/TolC gblock was most likely synthesized incorrectly, as it was not able to be integrated into the vector backbone. We would improve this by ordering a new gblock and starting the experiments again. Our next step would be to hopefully complete these experiments we had planned for this summer in the lab. Unexpected results that could come up could be due to *S. alvi’s* specific growth requirements - 5% CO2 , as well as certain types of media. We were growing *S. alvi* in LB, but it has been known to grow better in more supplemental media, such as blood agar or heart infusion agar [5]. This is something we could test to improve the results of our experiments. If the secretion was unsuccessful, we could try testing a different Type 1 secretion system in *S. alvi*, such as the Has secretion system. If the genome integration experiment failed to produce an appreciable level of fluorescence, we could try inserting the GFP gene into another location on the chromosome. If our experiments are successful, we hope to further test our secretion plasmid by inserting a detoxification enzyme into the construct to be secreted. We would then confirm that this enzyme was secreted and active in detoxifying a substrate. Our next step would be to integrate a biocontainment mechanism into our chassis, using CRISPR. More information on our future steps and their experimental design can be found on our [Proposed Implementation](https://2020.igem.org/Team%3AMichiganState/Implementation) page.

# Bioinformatics

## Transcriptomics Experimental Design

Previous research conducted has shown evidence of imidacloprid detoxification by some species of soil bacteria. However, specific genes and proteins involved in the detoxification of imidacloprid in bacteria are not entirely known. We developed protocols for a transcriptomics study to identify the specific genes involved in imidacloprid detoxification in soil microbes that are capable of detoxifying imidacloprid. The protocol we developed is specifically for *P. putida* EM371. Through our metabolomics study, we have determined that *P. putida* EM371 is capable of imidacloprid detoxification. The end goal is to use RNA-Sequencing to identify all genes upregulated in the presence of imidacloprid.

Because RNA-seq is very expensive, we decided to use RT-qPCR to perform preliminary gene expression differences in genes that are likely to be involved in imidacloprid detoxification. Information on proteins involved in imidacloprid detoxification in soil bacteria is lacking; however, some proteins involved in neonicotinoid resistance in some insects are well documented. Through literature research, we identified that the three protein families for each phase of drug metabolism: P450s[1], glutathione S-transferases (GSTs)[2], and aldehyde oxidases (AOX)[3]. These proteins are also common enzymes that give insects resistance to neonicotinoids. Therefore, we assumed that soil bacteria that can grow in the presence of imidacloprid may also use these families of proteins.

We designed primers for the GST, p450, and AOX gene in *P. putida* EM371. Then, we designed primers for a housekeeping gene, *rpo*D, and a cellular response control, *ahp*C. Housekeeping genes are used as controls since their expression levels should not change when growth conditions change. The housekeeping gene, *rpo*D, is the sigma factor for RNA polymerase and has been used as a reference gene in previous studies involving Pseudomonas[4]. The cellular response gene, *ahp*C, is an oxidative response gene that has been found to be upregulated in *Pseudomonas* in the presence of ampicillin and MTBE[5]. When conducting the experiment, a control culture (without imidacloprid, ampicillin, or MTBE), cultures with one of the three chemicals, cultures with two of the three chemicals, and a culture with all three chemicals should be grown; and RT-qPCR conducted on each sample condition. The control culture with no chemicals provides insight into gene regulation in ideal conditions without toxins. This will serve as a baseline for *rpo*D, *ahp*C, GST, p450, and AOX gene regulation. By running RT-qPCR for each chemical, we can discern between chemicals in which genes are up and down-regulated. Recording changes in the regulation of all genes for all conditions is extremely important for later statistical analysis. The combination of ampicillin and MTBE will provide important insight into the regulation changes of *ahp*C, a gene known to be upregulated when oxidative stressors are present. RT-qPCR should be run for a culture grown with all three chemicals because we know that *ahp*C, our cellular response gene, is upregulated when an oxidative stressor is present. If *ahp*C does not show signs of upregulation, then we know there is something wrong with the sample or the execution of the procedure. If the regulation of *rpo*D changes drastically from the baseline, then we know there is something wrong with the sample or the execution of the procedure. Troubleshooting the procedure will be required when unexpected results arise from the housekeeping gene and cellular response gene. If the GST, p450, or the AOX gene show no significant regulation changes with the addition of imidacloprid, more literature research will be needed. If *rhp*C significantly changes in the presence of imidacloprid, then some research efforts will be shifted towards *rhp*C.

If RT-qPCR provides statistically significant data, then we would proceed with RNA-seq. We would conduct RNA-Seq on samples grown without imidacloprid and samples with imidacloprid. Because of the price of RNA-seq, we are limited in the number of samples we would be able to submit.

To determine the concentration of ampicillin, MTBE, and imidacloprid to add to the cultures for RT-qPCR and RNA-seq, we would record growth curves of cultures with the various combinations of chemical stressors. A good starting point for deciding concentrations of each chemical would be to read the methods section of scientific journals that researched *rhp*C using ampicillin and MTBE and detoxification of imidacloprid by *Pseudomonas*. To evaluate more conditions, concentrations from the literature review can be concentrated and diluted by factors of two, five, and ten. The concentrations of each chemical must remain consistent for all the cultures (e.g. 100μM of imidacloprid is picked, then all cultures that are to have imidacloprid added to it should contain 100μM of imidacloprid). The growth of the culture containing all three chemicals will most likely determine the concentration of each chemical because the cells have to metabolize and defend themselves against three toxins.

## Metabolomics

Using data collected by LC-MS/MS and analyzed through the GNPS environment, we hope to develop a detailed snapshot of the metabolites generated by *P. putida* EM371 when exposed to imidacloprid. Using an untargeted approach, we are able to study both the metabolism of imidacloprid into various derivatives and any changes to other metabolic processes when *P. putida* is exposed to xenobiotic stress.

While we have generated promising preliminary results indicating that we are able to identify imidacloprid and imidacloprid metabolites within the extracellular environment of P. putida after a 96-hour biotransformation period, several parameters still must be optimized to take full advantage of the data generated by our metabolomics experiments.

A key limitation in the interpretation of our preliminary results was the carry-over of imidacloprid across numerous samples. During our first trial, we developed a protocol that exposed *P. putida* to 1.76 mM of imidacloprid which resulted in the saturation of our chromatography column. This over-saturation of imidacloprid caused imidacloprid to be detected in our negative control samples thereby limiting the interpretation of our trial. While we were not able to analyze the differences in metabolic profiles between the samples treated with and without imidacloprid, we did confirm the ability to detect imidacloprid in concentrations as low as 1 uM.

In future experiments we designed (from which we are still waiting on data), we have scaled-up the total number of imidacloprid concentrations to four concentrations (8.8 uM, 17.6 uM, 35.2 uM and 70.4 uM) to optimize the concentration of imidacloprid such that we will not induce imidacloprid carryover across our samples. Additionally, we have added a concentration gradient of imidacloprid to develop a standard curve based on the relationship of the chromatographic peak area and concentration of imidacloprid.

While the use of chromatographic information to quantify the concentration of a target compound is not a novel idea within iGEM, new computational methods have been released that allow for the use of this chromatographic information to improve the process of generating molecular networks. This new approach, called feature-based molecular networking, is explained in more detail in our [Modeling page](https://2020.igem.org/Team%3AMichiganState/Model).

## Multi-omics Approach

For most of our wiki, we have discussed metabolomics and transcriptomics as two distinct and separate analytical techniques, but the final desired outcome of this “pathway cracking” approach is the merging of information generated from both thrusts.

Through our metabolomics approaches (including both our classical and feature-based molecule networks) we are able to generate networks consisting of nodes representing each unique metabolite and edges representing the relationship between each metabolite. In general, these edges represent biochemical reactions catalyzed by specific enzymes. Through our transcriptomics approaches, we hope to develop a list of genes that are potentially involved in the metabolism of imidacloprid.

By understanding the relationships between these metabolites and the genes involved in imidacloprid metabolism, we hope to develop a more detailed understanding of imidacloprid metabolism within simpler microbes that have a higher degree for engineering success in the future.

In addition to aiding our process of better understanding imidacloprid metabolism in soil microbes, we have envisioned this multi-omics approach as a potentially powerful tool for measuring the effectiveness of our Bee-tox probiotic within the complex gut environment of the bee.

# References

## Gene Engineering

1. Leonard, S. P., et al. “Genetic engineering of bee gut microbiome bacteria with a toolkit for modular assembly of broad-host-range plasmids”. 2018, ACS synthetic biology, vol. 7, no. 5, 1279-1290, doi: 10.1126/science.aax9039
2. Leonard, S. P., et al. “Engineered symbionts activate honey bee immunity and limit pathogens.” Science, vol. 367, no 6477, Jan. 2020, pp. 573-576
3. Delepelaire, P. “Type I secretion in gram-negative bacteria.” Biochimica et Biophysica Acta - Molecular Cell Research, vol. 1694, May 2004, pp. 149-161, doi: 10.1016/j.bbamcr.2004.05.001
4. Gentschev, I., et al. “The E. coli α-hemolysin secretion system and its use in vaccine development.” Trends in microbiology, vol 10, no. 1, Jan. 2002, pp 39-45, doi: 10.1016/S0966-842X(01)02259-4
5. Kwong, W, and Moran, N. “Cultivation and characterization of the gut symbionts of honey bees and bumble bees: description of Snodgrassella alvi gen. nov., sp. nov., a member of the family Neisseriaceae of the Betaproteobacteria, and Gilliamella apicola gen. nov., sp. nov., a member of Orbaceae fam. nov., Orbales ord. nov., a sister taxon to the order ‘Enterobacteriales’ of the Gammaproteobacteria.” International Journal of Systematic and Evolutionary Microbiology, vol. 63, no. 6, 2013, pp.2008-2018., doi: 10.1099/ijs.0.044875-0

## Bioinformatics

1. Roditakis, E., Morou, E., Tsagkarakou, A., Riga, M., Nauen, R., Paine, M., Morin, S., & Vontas, J. (2011). Assessment of the Bemisia tabaci CYP6CM1vQ transcript and protein levels in laboratory and field-derived imidacloprid-resistant insects and cross-metabolism potential of the recombinant enzyme. Insect Science, 18(1), 23–29.
2. Yang, X., He, C., Xie, W., Liu, Y., Xia, J., Yang, Z., ... & Yang, F. (2016). Glutathione S-transferases are involved in thiamethoxam resistance in the field whitefly Bemisia tabaci Q (Hemiptera: Aleyrodidae). Pesticide biochemistry and physiology, 134, 73-78.
3. Dick, R. A., Kanne, D. B., & Casida, J. E. (2005). Identification of aldehyde oxidase as the neonicotinoid nitroreductase. Chemical research in toxicology, 18(2), 317-323.
4. Savli, H., Karadenizli, A., Kolayli, F., Gundes, S., Ozbek, U., & Vahaboglu, H. (2003). Expression stability of six housekeeping genes: a proposal for resistance gene quantification studies of Pseudomonas aeruginosa by real-time quantitative RT-PCR. Journal of medical microbiology, 52(5), 403-408.
5. Yeom, J., Imlay, J. A., & Park, W. (2010). Iron homeostasis affects antibiotic-mediated cell death in Pseudomonas species. Journal of Biological Chemistry, 285(29), 22689-22695.

* [![Instagram](https://static.igem.org/mediawiki/2020/5/5b/T--MichiganState--IGIcon.png)](https://www.instagram.com/msuigem/)
* [![Twitter](https://static.igem.org/mediawiki/2020/a/ae/T--MichiganState--TwitterIcon.png)](https://twitter.com/msuigem)
* ![Email](https://static.igem.org/mediawiki/2020/a/ac/T--MichiganState--Emailicon.png)

[![](https://static.igem.org/mediawiki/2020/8/8b/HQ_page_logo.jpg)](https://2020.igem.org)

# Team:MichiganState/Experiments

[![](https://static.igem.org/mediawiki/2020/f/fa/T--MichiganState--BeeToxLogo.png)](https://2020.igem.org/Team%3AMichiganState)

Judging

[Judging Form](https://igem.org/2020_Judging_Form?id=3359)

[Safety](https://2020.igem.org/Team%3AMichiganState/Safety)

Awards

[Education](https://2020.igem.org/Team%3AMichiganState/Education)

Human Practices

[Human Practices](https://2020.igem.org/Team%3AMichiganState/Human_Practices)

[Integrated Human Practices](https://2020.igem.org/Team%3AMichiganState/Human_Practices/Integrated)

Project

[Contribution](https://2020.igem.org/Team%3AMichiganState/Contribution)

[Description](https://2020.igem.org/Team%3AMichiganState/Description)

[Design](https://2020.igem.org/Team%3AMichiganState/Design)

[Engineering](https://2020.igem.org/Team%3AMichiganState/Engineering)

[Experiments](https://2020.igem.org/Team%3AMichiganState/Experiments)

[Notebook](https://2020.igem.org/Team%3AMichiganState/Notebook)

[Implementation](https://2020.igem.org/Team%3AMichiganState/Implementation)

[Results](https://2020.igem.org/Team%3AMichiganState/Results)

Team

[Team](https://2020.igem.org/Team%3AMichiganState/Team)

[Attributions and Sponsors](https://2020.igem.org/Team%3AMichiganState/Attributions)

[Collaborations](https://2020.igem.org/Team%3AMichiganState/Collaborations)

# Experiments

![](https://static.igem.org/mediawiki/2020/1/12/T--MichiganState--bacteria.png) ![](https://static.igem.org/mediawiki/2020/3/38/T--MichiganState--eppendorf.png)

# Gene Engineering

Due to the COVID-19 pandemic and the project not being COVID oriented and an undergraduate team, we were not allowed to gain access to lab space at Michigan State University. However, with the help of Kathryne Ford and Shaylynn Miller, graduate students of the TerAvest lab, they were able to perform experiments for us. The below protocols are written and performed.

### Experimental Overview:

* Introduce mRFP plasmid (no secretion) into *Snodgrassella alvi (S. alvi)* and measure fluorescence levels.
* Introduce mRFP secretion plasmid into *S. alvi* and measure fluorescence levels of the supernatant.
* Introduce GFP gene into the *S. alvi* chromosome and measure fluorescence levels.

PCR

For amplifying genes from iGEM kit and to verify constructs

Materials

* Thermocycler
* Ice
* PCR tubes
* 10 µM Forward and Reverse Primers
* Template DNA
* 10x Standard Q5 reaction buffer
* Q5 polymerase
* 10 mM dNTPS
* dH2O

Procedure

1. Put PCR tubes on ice.
2. Vortex all reagents and keep on ice.

##### For each 50 μL PCR tube:

1. 2 μL Template DNA (10 ng-500 ng)
2. 5 μl 10X Q5 buffer with MgCl2
3. 1 μl dNTP mix (10 mM)
4. 2.5 μL Forward Primer (10 μM stock)
5. 2.5 μL Reverse Primer (10 μM stock)
6. 0.5 μL Q5 DNA Polymerase (5 units/μL)
7. 36.5 μL Sterile dH2O (variable)
8. Gently mix the reaction.
9. Transfer the PCR tubes to the PCR machine with the block preheated to 95oC.

##### PCR Machine Settings

1. Initial denaturation for 2 min at 95oC
2. Denature for 30 sec at 95oC
3. Anneal for 30 sec at 55oC (annealing temperature will change with primers)
4. Extend for 2 min at 72oC (extension time will vary with product length)
5. Repeat for 25-30 cycles

Gibson Assembly

Materials

* Gibson Master Mix
* Backbone DNA
* Insert DNA
* Thermocycler
* dH2O

Procedure

##### Calculate the amount of DNA for each fragment:

pmols = (weight in ng) x 1,000 / (base pairs x 650 daltons) OR use NEBioCalculator.

Want 0.02 - 0.5 total pmol of DNA when assembling a fragment into a vector and 2-3:1 molar ratio of insert:backbone.

##### Setting up the reaction

1. Calculate the μL of backbone and insert to add, then add them both to a PCR tube.
2. Add dH2O to the PCR tube to have a final volume of 10 uL.
3. Add 10uL of NEB DNA Assembly Mix.
4. Incubate in a thermocycler for 1 hour.
5. Then store at -20oC until transformation.
6. To verify constructs, do PCR with the “check” primers, then run a gel to test their lengths. Can also do sequencing.

Single Restriction Digest

Materials

* 10x buffer (Cutsmart)
* DNA (1 ug/ 1 uL concentration)
* Restriction Enzyme
* Antarctic Phosphatase
* Antarctic Phosphatase Reaction Buffer (10x)
* dH2O water

Procedure

1. Add X uL of DNA to a clean tube (amount of DNA will depend on concentration).
2. Add 5 uL of 10x buffer.
3. Add 1 uL of restriction enzyme.
4. Add 2 uL Phosphatase Reaction Buffer (10x) and 5 units of Antarctic Phospatase.
5. Add dH2O to 50uL.
6. Incubate at 37oC for 2-3 hours.
7. Stop the digestion by heat inactivation at 80oC for 20-30 mins.

Transformation of *E. coli* Donor Strain

Materials

* Ice
* SOC Media (must contain DAP))
* LB Media (must contain DAP))
* Selection plates
* *E. coli* WM6026 (heat shock competent)
* DNA

Procedure

1. Thaw cells on ice.
2. Mix 1 - 5 μl of DNA (usually 10 pg - 100 ng) into 20-50 μL of competent cells in a microcentrifuge tube. GENTLY mix by flicking.
3. Leave on ice for 30 mins (no mixing).
4. Heat shock at 42oC for 30 seconds (no mixing) (may be a little longer).
5. Place tubes back on ice for 2 minutes.
6. Add 250 μL of room temp media to tube (SOC w/ DAP) .
7. Place the tube at 37oC for 60 minutes. Shake vigorously (250 rpm) or rotate.
8. Warm selection plates to 37oC.
9. Spread 50–100 µl of the cells and ligation mixture onto the plates (LB agar plates with DAP) and the appropriate antibiotic (Amp for pBTK510, Amp for pBTK599, Kan for pX2-Cas9) .
10. Incubate overnight at 37oC.

Conjugation of *S. alvi*

Materials

* *E. coli* WM6026 transformed with plasmid
* *S. alvi* wkB2
* TSA plates
* DAP
* Ampicillin
* PBS

Procedure

1. Grow *E. coli* WM6026 overnight, shaking with antibiotic (Ampicillin) and DAP (0.3 mM).

For chromosome integration: Ampicillin (pBTK599 *E. coli*) and Kanamycin (px2-Cas9 *E.coli*) Grow in LB

2. Grow *S. alvi* overnight in LB, in CO2 Sachet at 37oC.
3. Wash both strains in 1 mL PBS, spun down (3824g for 5 min) and resuspended in 1 mL of LB.
4. Combine the 2 suspensions in a 9:1 OD ratio (90 uL *S. alvi*: 10 uL *E. coli*) in a microcentrifuge tube, pipette twice to mix. Plate this 100 uL mixture on a LB plate with 0.3 mM DAP.
5. Incubate the conjugation plate for 8 hours.
6. Scrape the conjugation mixture into a microcentrifuge tube with 1 mL of PBS. Vortex and spin down again (3824g).
7. Plate 100 μL of of this mixture and of 1:10 dilutions and 1:100 dilutions of this mixture onto LB plates without DAP and incubate for 2-3 days.
8. Restreak single colonies on Ampicillin plate without DAP.
9. Pick single colonies and restreak on antibiotic plates. Confirm transfer of the plasmid via PCR amplification of the insert.

Measuring Fluorescence

Materials

* 24 Well Plate
* Pipette and tips
* PBS Buffer
* Fluorescein

Procedure

##### Separating Cells

1. Prepare stock solution of reference dye.
2. Make 10x fluorescein stock solution (100uM) by resuspending fluorescein powder in 1ml of 1xPBS.
3. Dilute 10x reference stock solution with 1x PBS to make 1x reference solution with concentation 10uM.
4. Prepare serial dilutions of fluorescein, consecutively transferring 1 mL from column to column with good mixing.
5. Add 1 mL of 1x PBS into wells A2-6, B1-6, C1-3, D1-3.
6. Add 2 mL of fluorescein 1x stock solution into A1.
7. Transfer 1 mL of fluorescein stock solution from A1 into A2.
8. Mix (pipette up and down 3x) and transfer 1 mL into A4.
9. Mix A4 and transfer 1 mL into A5.
10. Mix A5 and transfer 1 mL into A6.
11. Mix A6 and transfer 1 mL into B1.
12. Mix B1 and transfer 1 mL into B2.
13. Mix B2 and transfer 1 mL into B3.
14. Mix B3 and transfer 1 mL into B4.
15. Mix B4 and transfer 1 mL into B5.
16. Mix B5 and transfer 1 mL into liquid waste.
17. Add 1 mL of samples into wells.
18. C1-3 Cell Replicates.
19. D1-3 Replicates of supernatant.
20. D4-6 Replicates of negative control (water).
21. Insert plate into a plate reader to obtain a standard curve of the serial dilution and fluorescent levels of supernatant sample.

1. Abs600 (OD)
2. Compare supernatant sample data to standard curve to analyze relative fluorescent signal

Adapted from iGEM protocols.io Plate Reader Fluorescence Calibration V.3

# Device Design

Due to the COVID-19 pandemic and the with most of Michigan State University's facilities shut down to undergraduates in the summer, we struggled to gain administrative approval for our device experiments. Despite those challenges, we've managed to outline methods on how to approach collecting data on the bees and bee attraction of our device.

### Objectives:

* Quantify bee attraction to bee feeding devices.
* Revisit design decisions made and search for better solutions.
* Calibrate image recognition, return increasing confidence intervals.

Mini-Prototype Testing

##### Purpose:

To evaluate the impact of several variables on bee attraction to the feeders for design optimization.

Materials

* 1:1 sucrose solution by mass
* Feeders, numbered 1-5
* Evaporation control feeder
* Table
* Meter stick
* 50 mL graduated cylinder
* Funnel
* Video cameras

Procedure

1. Make 250 mL of 1:1 sucrose:water by volume.
2. Distribute 50 mL sucrose water to each feeder. Weigh each feeder before and after filling and record.
3. Position feeders outdoors on an elevated surface equidistant from one another, positioning feeding holes the same distance from the edge of the table. Feeding holes should be positioned facing outwards. Leave feeders out between 11:00 AM and 12:00 PM for a one-hour acclimation period. Place a paper plate beneath any designs with a leakage risk, to be weighed before and after. The feeder position should be rotated or randomized for each trial.
4. Record hourly sunlight, temperature, humidity, and wind speed in data collection document.
5. 12:00 PM: Position cameras 0.5 m away from feeding holes and start video recording for a two hour observation period. Continue recording hourly sunlight, temperature, humidity, and wind speed information.
6. 2:00 PM: Stop video cameras and remove cameras from vicinity. Continue recording hourly environmental information.
7. Bring feeders indoors, rinse and/or wipe down, dry reservoirs and feeding holes.
8. Watch videos and record number of bee visits at each feeder for every half hour, as well as visits from other insects if visible. Record qualitative observations regarding bee behavior at each feeder. Record time of each visit in seconds.

Final Prototype Testing Protocol

##### Introduction

The purpose of this protocol is to assess prototype quality based on functionality and relative bee attraction. The image recognition system will be considered successful if the gate opens for >75% of wild bees and less than 10% of non-bee insects. The feeding method will be considered successful if bees appear to be able to forage from the feeder, assessed qualitatively, and by relative weights of feeders before and after the observation period. Attractive elements will be considered successful if the complete prototype outfitted with acrylic covers has >75% of the number of bee visits of the control prototype and >40% of the number of bee visits of the open control.

Materials

* 3D printed prototype

+ Base (including reservoir and feeding sites) 3D printed in dental resin
+ Acrylic covers: 1 cover with holes over the entire prototype , 1 cover controled by motor components over feeding sites.
+ Motor and image recognition components
+ Plastic stoppers

* Control prototype

+ 3D printed base in dental resin, identical to above. No acrylic covers or motor components.

* Open control

+ Shallow plastic dish, entirely open to the environment

* 1:1 sucrose water solution

+ Prepared by bringing 500 mL distilled water to a boil and stirring in 500 mL granulated sucrose until fully incorporated.
+ **Does not contain probiotic.**

* Graduated cylinder
* Funnel
* Measuring tape
* Cameras (3 preferable)

+ 720p resolution, 30fps. Audio recording not necessary.
+ Tripods may be necessary

* 4x4 in wooden blocks

+ Cut to equal lengths at the appropriate height based on the elevation of the herb level.

* Electronic balance
* Lemongrass oil

+ May be used as an attractant in equal volumes on each feeder if initial trials do not yield sufficient data.

Procedure

##### Feeder preparation:

1. Turn prototypes to fill from the bottom with 300mL sucrose-water feeding solution each. Plug with plastic stoppers.
2. Fill open control with 300mL sucrose-water solution.
3. Add 0.2mL lemongrass oil to the surface of each feeder as an attractant, if necessary.
4. Record the mass in grams of each feeder.
5. Position each feeder atop a wooden block level with the herb layer. Position each feeder 2 ft apart. Randomize position of feeders for each trial.

##### Observation period:

1. Position each camera on a tripod such that it is focused on one of the three feeders.
2. Beginning at approximately 10AM, record video for 4 hours. Check cameras to ensure continuous filming every half hour and replace/recharge cameras as necessary.
3. Record the mass in grams of each feeder.

##### Data collection:

1. Review footage at no greater than 3x playback speed. In a spreadsheet, record the timestamp of each bee visit and the corresponding feeder. Record qualitative observations regarding bee behavior with a focus on apparent feeding success.
2. For the feeder equipped with image recognition, also record number of bee visits that resulted in successful recognition and gate opening, number of bee visits that did not result in successful recognition and gate opening, number of false openings (for objects or insects other than bees), and any qualitative observations related to general functionality.

# Bioinformatics

Given the limited time we had in the lab due to the COVID-19 pandemic, we spent a large portion of our time designing the experiments outlined below such that future teams can incorporate the “Pathway-Cracking” strategy approached by MichiganState’s Bioinformatics team. Experiments we were able to accomplish are denoted with the mark, (\*), whereas all other experiments have merely been outlined.

To provide a framework for future readers of this page, we have outlined our objectives and questions we hope to address through our experiments.

### Objectives:

* To make a more informed decision on which imidacloprid-degrading enzyme to incorporate into a Bee probiotic (“Bee-tox”).
* To better understand the process imidacloprid degradation by the soil microbe *Pseudomonas putida* EM371 (a derivative of *Pseudomonas putida* KT2440). Throughout the rest of this page, we refer to EM371 whenever we mention “*P. putida*” unless otherwise stated.

+ Develop a metabolomic profile of EM371 to identify potential imidacloprid metabolites.
+ Develop transcriptomic profile of EM 371 with RT-qPCR and RNA-seq to identify potential imidacloprid degrading enzymes.

### Experiments/Protocols and associated Questions:

#### Imidacloprid Biotransformation Experiment

* Does imidacloprid degradation only occur in the presence of *P. putida*?

##### Metabolomics (i.e. LC-MS/MS) (\*)

###### Classical Molecular Networking (\*)

* Can we detect Imidacloprid w/ LC-MS/MS?
* Can we detect Imidacloprid derivatives w/ LC-MS/MS?

###### Feature-Based Molecular Networking (\*)

* Can we detect Imidacloprid w/ LC-MS/MS?
* Can we detect Imidacloprid derivatives w/ LC-MS/MS?
* Can we semi-quantitatively characterize the degradation of Imidacloprid?

##### Transcriptomics

###### RT-qPCR Outline

* Is there a change in the transcriptomic profile of *P. putida* when exposed to imidacloprid?
* Can we observe an upregulation/downregulation in specific genes/gene families known to metabolize imidacloprid?

###### RNA-seq Outline

* What genes are upregulated/downregulated in the presence of Imidacloprid?
* Are there any additional differences in transcriptome profiles of cells exposed to various xenobiotics (compared to the profile developed through RT-qPCR)?

Imidacloprid Biotransformation Experiment

Protocol adapted from “Regulation of Hydroxylation and Nitroreduction Pathways during Metabolism of the Neonicotinoid Insecticide Imidacloprid by *Pseudomonas putida* '' with several modifications [1]. Experiment designed by members of the Bioinformatics Sub-group; but due to university regulations, conducted by members of Dr. Michaela TerAvest’s Lab (with particular shout outs for Shaylynn Miller, Nicholas Telft, and Kati Ford).

Materials

* *P. putida* EM371
* LB Broth (1 L) (Miller Formulation)

+ 10.0 g NaCl
+ 5.0 g Yeast Extract
+ 10.0 g peptone

* LB Agar Plates (Miller)
* 0.2 M Sodium Phosphate Buffer [Na-PB] (pH 8.0) (1 L)

+ 1.463 g\*\*\* Monobasic Sodium Phosphate [NaH2PO4 · H2O] (MW = 137.99 g/mol) (5.3%\*\*)
+ 26.887 g\*\*\* Dibasic Sodium Phosphate [Na2HPO4] (MW=141.96) (94.7%\*\*)

\*\*\*: The MW for the reagents are based on the specific sodium phosphate hydrates in stock, adjust the mass if needed based on the Sodium Phosphate Hydrates accessible.
\*\*: These percentages (ratio of the monobasic to dibasic sodium phosphate) should result in a pH ~8, adjust pH as needed

* IMI Stock Solution (1 mL) [~0.2 M (176 mM)]

Filter sterilize

+ 45 mg Imidacloprid (Sigma Cat. 37894; CAS:138261-41-3) in 1 mL acetonitrile [HPLC Grade]

* Glucose Stock Solution (1 mL) [1 M]

Filter sterilize

+ Add 7.2g glucose to 40 mL ddH2O

* Methanol [HPLC Grade]

keep ice cold

* Tubes and flasks

+ 15 mL falcon tubes
+ 50 mL falcon tubes
+ 1.5 mL microcentrifuge tubes
+ 500 mL Flask
+ 50 mL Centrifuge Tubes
+ 96-well plate

Procedure

##### Culture Growth:

1. Streak bacteria on LB agar plates and incubate at 30oC until single colonies appear
2. Inoculate a single colony into a 20-mL test tube containing 3 mL LB broth and incubate at 220 rpm for 24 hours at 30oC
3. Prepare two 500-mL flasks with 100 mL of LB broth. Transfer 1 mL of culture into each flask.
4. Incubate for 6 hours at 30oC. [expected OD600 ~4-5]
5. Harvest cells by centrifugation at 6,000 x g for 10 minutes
6. Wash cells with 0.2 M Na-PB (pH 8.0) PBS, and spin down cells for 10 minutes at 6,000 x g
7. Prepare *P. putida* samples

1. Label samples according to table 1 below.
2. Resuspend cells to an OD600 of 5.0 in LB broth, and aliquot into 4 16-mL aliquots in 50-mL falcon tubes and add the following reagents to the tubes and label accordingly.
3. After adding Imidacloprid and/or glucose, aliquot 5-mL of each master mix into 3 50-mL falcon tubes.

Table 1. *P. putida* Samples

| Condition | Imidacloprid [~1.76 mM] (uL 100x IMI Stock) | Glucose [10 mM] (uL of 1M Glucose) |
| A- (Tubes A-[1-3]) |  |  |
| A+ (Tubes A+1-3)\* | 160 |  |
| B- |  | 160 |
| B+ | 160 | 160 |

\*Tube labeling examples: for condition A-, label tubes A-1, A-2, and A-3. For condition A+, label tubes A+1, A+2, and A+3

8. Prepare blanks (Samples containing everything except the cells)

1. Label 4 x 50-mL falcon tubes C1-C4.
2. Add 6 mL LB broth to each tube and the following reagents according to table 2 below.

Table 2. Blank Samples

| Condition | Imidacloprid [1.76 mM] (uL 0.2 M IMI) | Glucose [10 mM] (uL of 1M Glucose) |
| C1 |  |  |
| C2 | 50 |  |
| C3 |  | 50 |
| C4 | 50 | 50 |

9. Transfer ~1 mL from each "condition" as a time 0 sample
10. Incubate samples for 96 hours at 220 rpm and 30oC
11. Measure OD600 after 96-incubation period

##### Prepare Samples for LC-MS/MS:

1. After IMI transformation period of 96 hours, spin-down cells at 10,000 x g for 10 minutes.
2. Collect the supernatant, and aliquot 200 uL from the samples into 1.5 mL centrifuge tubes.
3. Add 400 uL of ice cold methanol to each sample.

**The Specific Benchling Notebook entries for each trial can be found through the following links, and any changes to the above protocol are mentioned in brief:**

 [Trial 1](https://benchling.com/s/etr-t4reB4qzIRZ8Ju4oAal8)

 [Trial 2](https://benchling.com/s/etr-MsMunuXx9xf25tXRbr5A)

* Repeated experiment in a 24-well plate and discarded the Glucose sample.
* Reduced IMI concentration and Exposed *P. putida* samples to various concentrations of IMI including 8.8 uM, 17.6 uM, 35.2 uM and 70.4 uM.
* Included an IMI standard gradient including concentrations of 1 uM, 0.75 uM, 0.5 uM, 0.25 uM, and 0.125 uM.

 [Trial 3](https://benchling.com/s/etr-NmrCA4857ZfhEFisjn2q)

* Repeat of Trial 2 due to Contamination in Blank samples.

 [Trial 4](https://benchling.com/s/etr-mbYoyw9dqtqb8CKwVoyF)

* Repeat of Trial 2 with Imidacloprid reagents filter sterilized.

Metabolomics (i.e. LC-MS/MS)

The methods for obtaining mass spectra and liquid chromatography data are summarized below, and these methods are adapted from our mentor's paper “Metabolomic signatures of coral bleaching history” [2]. This experiment was performed by members of the Quinn Laboratory.

Materials

##### Instument:

Thermo™ QExactive™ coupled to a Vanquish Ultra High-Performance Liquid Chromatography (UHPLC) system

##### Mobile Phase:

Channel A: 0.1 % Formic Acid in Milli-Q Water

 Channel B: Acetonitrile

##### Stationary Phase:

Reverse Phase UPLC BEH C-18 column, 2.1 mm × 100 mm (Waters® Acquity® (Wood Dale, IL, USA))

LC-MS/MS Data Collection:

##### 12 Minute Chromatographic Runs with the following linear Gradients

* Time 0-1 min → 2% Channel B
* Time 1-8 min → 2-100% Channel B
* Time 8-10 min → 100% Channel B
* Time 10-12 min → 2% Channel B

##### Injection Volume of 10 uL

Flow Rate 0.40 mL/min @ 60oC

##### Mass Spec

* Electrospray Ionization in Positive Mode
* m/z scan range: 100 - 1500

Molecular Networking

The files generated from mass spectrometry were converted into the open format .mzML and subsequently analyzed with Classical Molecular Networking powered by the GNPS (“Global Natural Products Social Molecular Networking”) environment and with Feature-Based Molecular Networking using MZmine (v. 2.53) and GNPS. Both of these processes result in the formation of networks consisting of nodes representing each unique metabolite and edges representing the relationship between each metabolite. But, as explained in more depth in our Modeling page, classical molecular networking relies primarily on the relationships between the MS2 spectra of each compound while feature-based molecular networking incorporates chromatographic information when identifying individual compounds within an analyte. The protocols used for these data analysis steps are briefly outlined below.

Classical Molecular Networking

While more in-depth documentation can be found in [GNPS’s documentation website](https://ccms-ucsd.github.io/GNPSDocumentation/), we have briefly outlined the process of generating classical molecular networks.

1. Create a GNPS account and log in.
2. On the home page of GNPS, under “Molecular Networking” in the “Data Analysis” section, click “Create Molecular Network”.
3. Under workflow selection, give your Job a name.
4. Under “Basic Options” click on the “Select Input Files” and the following pop-up should appear. You should be able to upload files and assign files to input boxes:
![](https://static.igem.org/mediawiki/2020/b/be/T--MichiganState--GNPS_1.png)5. After uploading your input files, under “Advanced Network Options” add a Metadata file with appropriate identifying information for each group/sample.
6. Leave other settings to default values, and add your email to the “Workflow Submission” section before clicking “Submit”.
7. Visualize and analyze data on the Job Status page once workflow is complete. (An example Job Submission page for our project can be viewed through this [link](https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=b38b9d7f246b47108897cd8a61e357c0))
8. Under the link [“View Spectral Families (in Browser Network Visualizer)”](https://gnps.ucsd.edu/ProteoSAFe/result.jsp?task=b38b9d7f246b47108897cd8a61e357c0&view=network_components), we can find the cluster ID for Imidacloprid and view it’s molecular network by searching for “Imidacloprid” under the column for “AllID”.

By clicking “Visualize Network” link in the “Visualize Network” column for the row corresponding to imidacloprid, we can visualize cluster of compounds related to Imidacloprid (linked [here](https://gnps.ucsd.edu/ProteoSAFe/result.jsp?view=network_displayer&componentindex=70&task=b38b9d7f246b47108897cd8a61e357c0#%7B%7D)):

Feature-Based Molecular Networking

Using the recently released features in the GNPS environment, we were able to incorporate chromatographic and isotopic data into our molecular networks with feature-based molecular networking methods (FBMN). This summer we focused on creating FBMNs using MZmine2 and GNPS. A more-indepth protocol can be found in this [GNPS documentation article](https://ccms-ucsd.github.io/GNPSDocumentation/featurebasedmolecularnetworking-with-mzmine2/), but we’ve also produced a beginner’s guide to trying our FBMN workflow (along with .mzML and Metadata files) that can be found below or [here](https://static.igem.org/mediawiki/2020/c/cc/T--MichiganState--GNPS_Intro_Guide.pdf).

Transcriptomics

In order to identify potential imidacloprid degrading enzymes, we developed targeted and untargeted transcriptomics experiments with *P. putida* serving as our model organism. Using RT-qPCR, we hope to ascertain whether the exposure of imidacloprid induced a change in gene expression. Using RNA-seq in an untargeted approach, we hope to ascertain what genes are upregulated in the presence of Imidacloprid. For both experiments, we have yet to perform in-depth experimental troubleshooting, so only a rough outline for each experiment is provided below.

RT-qPCR

Genes of Interest:

In anticipation of potentially performing some experiments during the later stages of Summer, we found the following genes as markers for either oxidation stress response or imidacloprid degradation:

**AhpC**: A cellular marker for oxidative stress that has been found to be upregulated in the presence of the stressors ampicillin and MTBE [9, 10]

**rpoD**: RNA polymerase sigma factor rpoD has been characterized and utilized as a stable housekeeping gene used as a reference gene [11-13].

**AOX**: Previously characterized to catalyze the nitrogen reduction of Imidacloprid in P. putida KT2440 and mammalian systems [1, 14-15].

**P450**: Previously characterized to catalyze the hydroxylation of Imidacloprid [16-21]

**GST**: Involved in oxidative stress response and has been previously associated with imidacloprid resistant in Folsomia candida (a detoxifying soil microbe), Musca domestica (house flies), and Bombyx mori (silkworms) [22-26]

For each gene, we designed and ordered 3 pairs of primers for each sequence to be used for qPCR. These primers and the respective genes can be found attached to this genome sequence in this Benchling [link](https://benchling.com/s/seq-kyfk4ZVCfeKycW1237WP):

![](https://static.igem.org/mediawiki/2020/e/e2/T--MichiganState--Bioinf_Genes.jpg)

Protocol:

1. Isolate RNA from Cell Pellets derived from the “Imidacloprid Biotransformation” protocol using manufacturer instructions for the [Qiagen RNeasy Mini Kit](https://www.qiagen.com/us/products/discovery-and-translational-research/dna-rna-purification/rna-purification/total-rna/rneasy-mini-kit/#productdetails).
2. Perform quality control using a TapeStation (Agilent) system.
3. Synthesize cDNA using random Hexamer primers (Protocol Adapted from NEB Biolabs [“Typical cDNA Synthesis Protocol”](https://www.neb.com/protocols/2014/10/09/typical-cdna-synthesis-protocol)).

1. Prepare the following reaction mix on ice:

| Component | Final Concentration/Amount | Volume (uL) |
| --- | --- | --- |
| 10X Amplification Buffer | 1X | 2.5 |
| dNTP MIx (10 mM) | 0.5 mM | 1.25 |
| Random Primers (60 uM) | 6 uM | 2.5 |
| RNase Inhibitor, Murine | 20 units | 0.5 |
| WarmStart RTx Reverse Transcriptase | 20 units | 0.5 |
| Template RNA | 1 ug | *Varies* |
| RNase Free ddH20 |  | To a total reaction volume of 25 uL |

2. Mix by gently flicking the reaction mixture.
3. Incubate the reaction mix with the following temperatures and times:

| Step | Temperature (oC) | Time (Minutes) |
| --- | --- | --- |
| Annealing | 25 | 5 |
| Synthesis/Polymerization | 55 | 10 |
| Heat Inactivate | 80 | 10 |

4. [Using the SYBR Green System:](https://www.sigmaaldrich.com/technical-documents/protocols/biology/sybr-green-qpcr.html)

1. Set up the following reaction on ice for each gene of interest and sample condition:

| Component | Final Concentration/Amount | Volume (uL for 20 uL reaction) |
| --- | --- | --- |
| 2X qPCR mix | 1X | 10 |
| Forward Primer (10 uM) | 0.2 uM | 0.4 |
| Reverse Primer (10 uM) | 0.2 uM | 0.4 |
| MgCl2 (25 mM) | 3.5\* mM | 3.5 |
| Water |  | 1.7 |
| Template cDNA solution |  | 4 |

\*This concentration must be optimized for each primer pair.

2. Run the reaction on a compatible qPCR machine with the following cycle conditions:

| Step | # of Cycles | Temperature (oC) | Time (min:sec) |
| --- | --- | --- | --- |
| Initial Denaturation | 1 | 94 | 2:00 |
| Denaturation | 40 | 94 | 0:15 |
| Annealing, Extension and read fluorescence |  | ~60\* | 1:00 |
| Hold | 1 | 4 | inf |

\*This temperature must be optimized for each primer pair.

5. Analyze data according to instrument-specific instructions, and compare relative expression of selected genes in various conditions.

RNA-seq

1. Isolate RNA from Cell Pellets derived from the “Imidacloprid Biotransformation” protocol using manufacturer instructions for the [Qiagen RNeasy Mini Kit](https://www.qiagen.com/us/products/discovery-and-translational-research/dna-rna-purification/rna-purification/total-rna/rneasy-mini-kit/#productdetails).
2. Measure RNA quality using a TapeStation (Agilent) system.
3. Prepare libraries using the [Illumina TruSeq Stranded Total RNA Library kit](https://www.illumina.com/products/by-type/sequencing-kits/library-prep-kits/truseq-stranded-total-rna.html) with [Ribo-Zero Depletion](https://www.illumina.com/products/by-type/accessory-products/ribo-zero-plus-rrna-depletion.html) according to manufacturer’s instructions.
4. Sequence libraries using the Illumina HiSeq Sequencing System.
5. Process data using MSU’s HPCC resources and the following workflow:

1. Align reads to genome using Tophat
2. Assemble transcripts for each sample using cufflinks
3. Merge assemblies into a single transcriptome using cuffmerge
4. Quantify expression of transcripts using htseq-count
5. Identify differentially expressed genes based on environmental conditions using R 3.3.2
6. This workflow largely follows this Example RNA-seq job example outlined by ICER (Institute for Cyber Enabled Research) which provides HPCC support and services for MSU researchers. [Example Link](https://wiki.hpcc.msu.edu/display/ITH/RNA-seq%2Bexample)

## References

[1] Lu, T.-Q., Mao, S.-Y., Sun, S.-L., Yang, W.-L., Ge, F., & Dai, Y.-J. (2016). Regulation of Hydroxylation and Nitroreduction Pathways during Metabolism of the Neonicotinoid Insecticide Imidacloprid by Pseudomonas putida. https://doi.org/10.1021/acs.jafc.6b01376

[2] Roach, T. N. F., Dilworth, J., Martin, C., Jones, A. D., Quinn, R., & Drury, C. (2020). Metabolomic signatures of coral bleaching history. BioRxiv, 2020.05.10.087072. https://doi.org/10.1101/2020.05.10.087072

[3] Mingxun Wang, Jeremy J. Carver, Vanessa V. Phelan, Laura M. Sanchez, Neha Garg, Yao Peng, Don Duy Nguyen et al. "Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking." Nature biotechnology 34, no. 8 (2016): 828. PMID: 27504778

[4] Frank, Ari M., et al. "Clustering millions of tandem mass spectra." Journal of proteome research 7.01 (2007): 113-122.

[5] Katajamaa, M., Miettinen, J. & Oresic, M. MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics 22, 634–636 (2006).

[6] Myers OD, Sumner SJ, Li S, Barnes S, Du X: One Step Forward for Reducing False Positive and False Negative Compound Identifications from Mass Spectrometry Metabolomics Data: New Algorithms for Constructing Extracted Ion Chromatograms and Detecting Chromatographic Peaks. Anal Chem 2017, DOI: 10.1021/acs.analchem.7b00947

[7] Nothias, L., Petras, D., Schmid, R. et al. Feature-based molecular networking in the GNPS analysis environment. Nat Methods 17, 905–908 (2020). https://doi.org/10.1038/s41592-020-0933-6

[8] Pluskal, T., Castillo, S., Villar-Briones, A. & Oresic, M. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics 11, 395 (2010).

[9] Yeom, J., Imlay, J. A., & Park, W. (2010). Iron homeostasis affects antibiotic-mediated cell death in Pseudomonas species. Journal of Biological Chemistry, 285(29), 22689–22695. https://doi.org/10.1074/jbc.M110.127456

[10] Krayl, M., Benndorf, D., Loffhagen, N., & Babel, W. (2003). Use of proteomics and physiological characteristics to elucidate ecotoxic effects of methyltert-butyl ether in Pseudomonas putida KT2440. PROTEOMICS, 3(8), 1544–1552. https://doi.org/10.1002/pmic.200300477

[11] Savli, H., Karadenizli, A., Kolayli, F., Gundes, S., Ozbek, U., & Vahaboglu, H. (2003). Expression stability of six housekeeping genes: A proposal for resistance gene quantification studies of Pseudomonas aeruginosa by real-time quantitative RT-PCR. Journal of Medical Microbiology, 52(5), 403–408. https://doi.org/10.1099/jmm.0.05132-0

[12] Wang, Q., & Nomura, C. T. (2010). Monitoring differences in gene expression levels and polyhydroxyalkanoate (PHA) production in Pseudomonas putida KT2440 grown on different carbon sources. Journal of Bioscience and Bioengineering, 110(6), 653–659. https://doi.org/10.1016/j.jbiosc.2010.08.001

[13] Bai, B., Ren, J., Bai, F., & Hao, L. (2020). Selection and validation of reference genes for gene expression studies in Pseudomonas brassicacearum GS20 using real-time quantitative reverse transcription PCR. PLOS ONE, 15(1), e0227927. https://doi.org/10.1371/journal.pone.0227927

[14] Casida, J. E. (2011). Neonicotinoid metabolism: Compounds, substituents, pathways, enzymes, organisms, and relevance. Journal of Agricultural and Food Chemistry, 59(7), 2923–2931. https://doi.org/10.1021/jf102438c

[15] Honda, H., Tomizawa, M., & Casida, J. E. (2006). Neonicotinoid metabolic activation and inactivation established with coupled nicotinic receptor-CYP3A4 and -aldehyde oxidase systems. Toxicology Letters, 161(2), 108–114. https://doi.org/10.1016/j.toxlet.2005.08.004

[16] Antony, B., Johny, J., Abdelazim, M. M., Jakše, J., Al-Saleh, M. A., & Pain, A. (2019). Global transcriptome profiling and functional analysis reveal that tissue-specific constitutive overexpression of cytochrome P450s confers tolerance to imidacloprid in palm weevils in date palm fields. BMC Genomics, 20(1), 1–23. https://doi.org/10.1186/s12864-019-5837-4

[17] Markussen, M. D. K., & Kristensen, M. (2010). Cytochrome P450 monooxygenase-mediated neonicotinoid resistance in the house fly Musca domestica L. Pesticide Biochemistry and Physiology, 98(1), 50–58. https://doi.org/10.1016/j.pestbp.2010.04.012

[18] Cheesman, M. J., Traylor, M. J., Hilton, M. E., Richards, K. E., Taylor, M. C., Daborn, P. J., Russell, R. J., Gillam, E. M. J., & Oakeshott, J. G. (2013). Soluble and membrane-bound Drosophila melanogaster CYP6G1 expressed in Escherichia coli: Purification, activity, and binding properties toward multiple pesticides. Insect Biochemistry and Molecular Biology, 43(5), 455–465. https://doi.org/10.1016/j.ibmb.2013.02.003

[19] Joußen, N., Heckel, D. G., Haas, M., Schuphan, I., & Schmidt, B. (2008). Metabolism of imidacloprid and DDT by P450 CYP6G1 expressed in cell cultures of Nicotiana tabacum suggests detoxification of these insecticides in Cyp6g1-overexpressing strains of Drosophila melanogaster, leading to resistance. Pest Management Science, 64(1), 65–73. https://doi.org/10.1002/ps.1472

[20] Karunker, I., Morou, E., Nikou, D., Nauen, R., Sertchook, R., Stevenson, B. J., Paine, M. J. I., Morin, S., & Vontas, J. (2009). Structural model and functional characterization of the Bemisia tabaci CYP6CM1vQ, a cytochrome P450 associated with high levels of imidacloprid resistance. Insect Biochemistry and Molecular Biology, 39(10), 697–706. https://doi.org/10.1016/j.ibmb.2009.08.006

[21] Suchail, S., Debrauwer, L., & Belzunces, L. P. (2004). Metabolism of imidacloprid inApis mellifera. Pest Management Science, 60(3), 291–296. https://doi.org/10.1002/ps.772

[22] Sillapawattana, P., & Schäffer, A. (2017). Effects of imidacloprid on detoxifying enzyme glutathione S-transferase on Folsomia candida (Collembola). Environmental Science and Pollution Research, 24(12), 11111–11119. https://doi.org/10.1007/s11356-016-6686-1

[23] Yamamoto, K., & Yamada, N. (2016). Identification of a diazinon-metabolizing glutathione S-transferase in the silkworm, Bombyx mori. Scientific Reports, 6(1), 1–9. https://doi.org/10.1038/srep30073

[24] Enayati, A. A., Ranson, H., & Hemingway, J. (2005). Insect glutathione transferases and insecticide resistance. In Insect Molecular Biology (Vol. 14, Issue 1, pp. 3–8). John Wiley & Sons, Ltd. https://doi.org/10.1111/j.1365-2583.2004.00529.x

[25] Reid, W. R., Sun, H., Becnel, J. J., Clark, A. G., & Scott, J. G. (2019). Overexpression of a glutathione S-transferase ( Mdgst ) and a galactosyltransferase - like gene ( Mdgt1 ) is responsible for imidacloprid resistance in house flies. Pest Management Science, 75(1), 37–44. https://doi.org/10.1002/ps.5125

[26] Ge, W., Yan, S., Wang, J., Zhu, L., Chen, A., & Wang, J. (2015). Oxidative stress and DNA damage induced by imidacloprid in zebrafish (Danio rerio). Journal of Agricultural and Food Chemistry, 63(6), 1856–1862. https://doi.org/10.1021/jf504895h

* [![Instagram](https://static.igem.org/mediawiki/2020/5/5b/T--MichiganState--IGIcon.png)](https://www.instagram.com/msuigem/)
* [![Twitter](https://static.igem.org/mediawiki/2020/a/ae/T--MichiganState--TwitterIcon.png)](https://twitter.com/msuigem)
* ![Email](https://static.igem.org/mediawiki/2020/a/ac/T--MichiganState--Emailicon.png)

[![](https://static.igem.org/mediawiki/2020/8/8b/HQ_page_logo.jpg)](https://2020.igem.org)

# Team:MichiganState/Results

[![](https://static.igem.org/mediawiki/2020/f/fa/T--MichiganState--BeeToxLogo.png)](https://2020.igem.org/Team%3AMichiganState)

Judging

[Judging Form](https://igem.org/2020_Judging_Form?id=3359)

[Safety](https://2020.igem.org/Team%3AMichiganState/Safety)

Awards

[Education](https://2020.igem.org/Team%3AMichiganState/Education)

Human Practices

[Human Practices](https://2020.igem.org/Team%3AMichiganState/Human_Practices)

[Integrated Human Practices](https://2020.igem.org/Team%3AMichiganState/Human_Practices/Integrated)

Project

[Contribution](https://2020.igem.org/Team%3AMichiganState/Contribution)

[Description](https://2020.igem.org/Team%3AMichiganState/Description)

[Design](https://2020.igem.org/Team%3AMichiganState/Design)

[Engineering](https://2020.igem.org/Team%3AMichiganState/Engineering)

[Experiments](https://2020.igem.org/Team%3AMichiganState/Experiments)

[Notebook](https://2020.igem.org/Team%3AMichiganState/Notebook)

[Implementation](https://2020.igem.org/Team%3AMichiganState/Implementation)

[Results](https://2020.igem.org/Team%3AMichiganState/Results)

Team

[Team](https://2020.igem.org/Team%3AMichiganState/Team)

[Attributions and Sponsors](https://2020.igem.org/Team%3AMichiganState/Attributions)

[Collaborations](https://2020.igem.org/Team%3AMichiganState/Collaborations)

# Results

![](https://static.igem.org/mediawiki/2020/a/ae/T--MichiganState--petridish.png)
![](https://static.igem.org/mediawiki/2020/a/ae/T--MichiganState--petridish.png)
![](https://static.igem.org/mediawiki/2020/a/ae/T--MichiganState--petridish.png)

# Bioinformatics

## Growth of *Pseudomonas putida* EM371 With Various Concentrations of Imidacloprid

From our literature review, *Pseudomonas putida* EM371 is expected to grow with imidacloprid (IMI). We grew *P. putida* EM371 with increasing concentrations of IMI to determine the concentration when IMI becomes toxic to *P. putida* EM371. *P. putida* EM371 with 8.8μM, 17.6μM, 23.5μM, and 35.2μM IMI in LB media grows at the same rate as *P.putida* EM371 without IMI. The growth of *P. putida* EM371 drops by an OD600 of 0.1 when inoculated in LB media with 70.4μM IMI. The growth is reduced, but not completely inhibited.

![](https://static.igem.org/mediawiki/2020/d/da/T--MichiganState--Bioinformatics_results_1.jpg)Figure 1: *Pseudomonas putida* EM371 grown in LB media with varying concentrations of imidacloprid.

## Transformation of Imidacloprid by *P. putida* EM371

From our literature review, we found that imidacloprid can be metabolized by three different pathways.

![](https://static.igem.org/mediawiki/2020/3/33/T--MichiganState--Bioinformatics_results_2.jpg)Figure 2: The three known pathways of microbial metabolism of imidacloprid[1].

To determine if *P. putida* EM371 metabolizes IMI in any way, we conducted LC-MS/MS and uploaded the data to GNPS. GNPS identified the presence of three compounds from the above diagram: imidacloprid (IUPAC name: (NE)-N-[1-[(6-chloropyridin-3-yl)methyl]imidazolidin-2-ylidene]nitramide), imidacloprid-guanidine (IUPAC name: 1-[(6-chloropyridin-3-yl)methyl]-4,5-dihydroimidazol-2-amine), and imidacloprid-urea (IUPAC name: 1-[(6-chloropyridin-3-yl)methyl]imidazolidin-2-one). The xanthine dehydrogenase, a protein in the aldehyde oxidase family, in *P. putida* Z-4 has been associated with the nitro reduction pathway[1]. Therefore, it is highly suspected that *P. putida* EM371 uses the nitro-reduction pathway for metabolizing imidacloprid. RT-qPCR on xanthine dehydrogenase could verify this claim.

Nitroso-imidacloprid and 6-CNA were not detected. Nitroso-imidacloprid and 6-CNA are possibly short lived intermediates. Metabolites from the hydroxylation pathway were not detected. Therefore, the hydroxylation is most likely not used by *P. putida* EM371 under these conditions.

# Device

The final results for the device team resulted from the synthesis of hardware and software components of our research: the physical design of a 3D printed bee feeder, and the development of image recognition software to control feeder access.

## Image recognition

For image recognition, we needed to use an open-source image recognition API that was available. This is where the Google Vision API would come in. The Google Vision API allows for both remote and local processing of images and comes with a variety of vision capabilities. It is able to provide confidence intervals of object recognition and label recognition for images. Additionally, the vision API allows for both local and remote image processing.

![](https://static.igem.org/mediawiki/2020/f/fa/T--MichiganState--Image_Rec_Flow_1.png)Figure 3: The initial flowchart for the image recognition program.

In Figure 3, the initial approach to image recognition would be done locally on the Raspberry Pi. The idea was to take a picture, process the image on the Raspberry Pi and return labels and their confidence intervals, and from the list of labels and confidence intervals, decide whether or not there would be a motor output. The vision API was tested on a personal laptop to experiment and test for desired outputs. We found that the best way to increase confidence intervals and to reduce unnecessary labels outputted was to have a solid color background to reduce the amount of noise in the image. The benefit of this method is that it allows the user to explore programming and the Raspberry Pi features in a simple manner. The immediate problem was that a network connection would be required at all times. Another problem is that all the data would be stored locally, where in the event of an accident, data would be irretrievable. This is where Figure 4 is introduced.

![](https://static.igem.org/mediawiki/2020/f/fa/T--MichiganState--Image_Rec_Flow_2.png)Figure 4: The final flowchart for the image recognition program.

The behavior is similar to Figure 3, but data is sent to the private web server and the server would return desired values, reducing the amount of data stored locally on the Raspberry Pi. This method is by far more challenging to learn for those who have not learned to use a Raspberry Pi for web hosting. However, it relieves the need for unnecessary packages to be installed and can simplify the amount of storage needed to process an image.

Unfortunately, the team was not able to create a finalized private web server with the Google Vision API implemented to carry out the desired behavior. However, we hope future teams can improve this approach or apply image recognition in a similar fashion.

## 3D Printed Feeder

The Device subteam designed a bee feeder that could be used to selectively administer the Bee-tox probiotic. This device was designed to be attractive to bees, while also featuring image recognition technology and motor components that would stop other pests from accessing the probiotic. After significant research detailed in our [Project Design](https://2020.igem.org/Team%3AMichiganState/Design) page, we successfully created our feeder. The motor cover and feeder were printed in resin.

![](https://static.igem.org/mediawiki/2020/thumb/6/6a/T--MichiganState--feeder-1.jpg/900px-T--MichiganState--feeder-1.jpg)
![](https://static.igem.org/mediawiki/2020/thumb/0/0e/T--MichiganState--feeder-2.jpg/1600px-T--MichiganState--feeder-2.jpg)

This final feeder prototype includes 4 feeding sites with diameters of 3mm.External dimensions of this feeder are 10cm x 10cm x 6.5 cm. The filling and drainage holes are both 0.6 cm in diameter. The internal tank dimensions are 9.5 cm x 2.9 cm x 9.5 cm, leading to an approximate maximum volume of over 260 cm^3. The feeder and motor cover .stl files to download can be found on our [Contribution Page](https://2020.igem.org/Team%3AMichiganState/Contribution).

## Final device with motor components

The complete device includes an outer acrylic cover to restrict access to potential predators while allowing entry for bees and smaller insects. It also includes a sliding cover over the feeding sites to restrict access to the probiotic for non-target organisms such as agricultural pests. The sliding cover is designed to open only when the image recognition system detects a bee present, and to close only when the bee is no longer close to the feeding sites.

![](https://static.igem.org/mediawiki/2020/b/be/T--MichiganState--complete-feeder-1.jpg)
![](https://static.igem.org/mediawiki/2020/e/e5/T--MichiganState--complete-feeder-2.jpg)

The device was able to hold and dispense water successfully. In future iterations, however, we would modify the design to have a tapered channel that would reduce air bubble formation near the feeding holes.

## Procedure for testing device

As mentioned in our Project Design page, our initial plan was to conduct experimental trials to determine the ideal color, feeding angle, and other feeder parameters that would optimize bee attraction. Unfortunately, due to several setbacks in the approval process, both through iGEM and our university, there was not enough time to conduct these trials. For future testing, we would use the procedure outlined in our [Experiments Page](https://2020.igem.org/Team%3AMichiganState/Experiments) for the Device team.

# References

1. Lu, T. Q., Mao, S. Y., Sun, S. L., Yang, W. L., Ge, F., & Dai, Y. J. (2016). Regulation of hydroxylation and nitroreduction pathways during metabolism of the neonicotinoid insecticide imidacloprid by Pseudomonas putida. Journal of agricultural and food chemistry, 64(24), 4866-4875.